Infectious Disease Modelling最新文献

Greening, or Huanglongbing (HLB), poses a severe threat to global citrus cultivation, affecting various citrus species and compromising fruit production. Primarily transmitted by psyllids during phloem feeding, the bacterium Candidatus Liberibacter induces detrimental symptoms, including leaf yellowing and reduced fruit quality. Given the limitations of conventional control strategies, the search for innovative approaches, such as resistant genotypes and early diagnostic methods, becomes essential for the sustainability of citrus cultivation. The development of predictive models, such as the one proposed in this study, is essential as it enables the estimation of the bacterium's concentration and the vulnerability of healthy plants to infection, which will be instrumental in determining the risk of HLB. This study proposes a prediction model utilizing environmental factors, including temperature, humidity, and precipitation, which play a decisive role in greening epidemiology, influencing the complex interaction among the pathogen, vector, and host plant. In the proposed modeling, it addresses non-linear relationships through cubic smoothing splines applications and tackles imbalanced categorical predictor variables, requiring the use of a random-effects regression model, incorporating a random intercept to account for variability across different groups and mitigate the risk of biased predictions. The model's ability to predict HLB incidence under varying climatic conditions provides a significant contribution to disease management, offering a strategic tool for early intervention and potentially reducing the spread of HLB. Using climatological and environmental data, the research aims to develop a predictive model, assessing the influence of these variables on the spread of Candidatus Liberibacter asiaticus, essential for effective disease management. The proposed flexible model demonstrates robust predictions for both training and test data, identifying climatological and environmental predictors influencing the dissemination of Candidatus Liberibacter asiaticus, the vascular bacterium associated with Huanglongbing (HLB) or greening.

In classical mumps models, individuals are generally assumed to be uniformly mixed (homogeneous), ignoring population heterogeneity (preference, activity, etc.). Age is the key to catching mixed patterns in developing mathematical models for mumps. A continuous heterogeneous age-structured model for mumps with vaccines has been developed in this paper. The stability of age-structured models is a difficult question. An explicit formula of R₀ was defined for the various mixing modes (isolation, proportional and heterogeneous mixing) with or without the vaccine. The results show that the endemic steady state is unique and locally stable if R₀ > 1 without any additional conditions. A number of numerical examples are given to support the theory.

Disease severity through an immunized population ensconced on a physical network topology is a key technique for preventing epidemic spreading. Its influence can be quantified by adjusting the common (basic) methodology for analyzing the percolation and connectivity of contact networks. Stochastic spreading properties are difficult to express, and physical networks significantly influence them. Visualizing physical networks is crucial for studying and intervening in disease transmission. The multi-agent simulation method is useful for measuring randomness, and this study explores stochastic characteristics of epidemic transmission in various homogeneous and heterogeneous networks. This work thoroughly explores stochastic characteristics of epidemic propagation in homogeneous and heterogeneous networks through extensive theoretical analysis (positivity and boundedness of solutions, disease-free equilibrium point, basic reproduction number, endemic equilibrium point, stability analysis) and multi-agent simulation approach using the Gilespie algorithm. Results show that Ring and Lattice networks have small stochastic variations in the ultimate epidemic size, while BA-SF networks have disease transmission starting before the threshold value. The theoretical and deterministic aftermaths strongly agree with multi-agent simulations (MAS) and could shed light on various multi-dynamic spreading process applications. The study also proposes a novel concept of void nodes, Empty nodes and disease severity, which reduces the incidence of contagious diseases through immunization and topologies.

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial¹ autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R² = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

Background

The number of people undergoing voluntary HIV testing has abruptly decreased since 2020. The geographical heterogeneity of HIV infection and the impact of COVID-19 on the diagnosis of HIV at regional level are important to understand. This study aimed to estimate the HIV incidence by geographical region and understand how the COVID-19 pandemic influenced diagnosis of HIV.

Methods

We used an extended back-calculation method to reconstruct the epidemiological dynamics of HIV/AIDS by geographical region. We used eight regions: Tokyo, the capital of Japan, Hokkaido plus Tohoku, Kanto plus Koshinetsu (excluding Tokyo), Hokuriku, Tokai, Kinki, Chugoku plus Shikoku, and Kyushu plus Okinawa. Four different epidemiological measurements were evaluated: (i) estimated HIV incidence, (ii) estimated rate of diagnosis, (iii) number of undiagnosed HIV infections, and (iv) proportion of HIV infections that had been diagnosed.

Results

The incidence of HIV/AIDS during the COVID-19 pandemic from 2020 to 2022 increased in all regions except Kanto/Koshinetsu (51.3 cases/year), Tokyo (183.9 cases/year), Hokuriku (1.0 cases/year), and Tokai (43.1 cases/year). The proportion of HIV infections that had been diagnosed only exceeded 90% in Tokyo (91.7%, 95% confidence interval (CI): 90.6, 93.3), Kanto/Koshinetsu (91.0%, 95% CI: 87.3, 97.8), and Kinki (92.5%, 95% CI: 90.4, 95.9). The proportion of infections that had been diagnosed was estimated at 83.3% (95% CI: 75.1, 98.7) in Chugoku/Shikoku and 80.5% (95% CI: 73.9, 91.0) in Kyusyu/Okinawa.

Conclusions

In urban regions with major metropolitan cities, including Tokyo, Kinki, and Kanto/Koshinetsu, the number of undiagnosed HIV infections is substantial. However, the proportion of undiagnosed infections was estimated to be smaller than in other regions. The diagnosed proportion was the lowest in Kyusyu/Okinawa (80.5%), followed by Chugoku/Shikoku and Hokkaido/Tohoku. The level of diagnosis in those regional prefectures may have been more influenced and damaged by the COVID-19 pandemic than in urban settings.

Background

An influenza forecasting system is critical to influenza epidemic preparedness. Low temperature has long been recognized as a condition favoring influenza epidemic, yet it fails to justify the summer influenza peak in tropics/subtropics. Recent studies have suggested that absolute humidity (AH) had a U-shape relationship with influenza survival and transmission across climate zones, indicating that a unified influenza forecasting system could be established for China with various climate conditions.

Methods

Our study has generated weekly influenza forecasts by season and type/subtype in northern and southern China from 2011 to 2021, using a forecasting system combining an AH-driven susceptible-infected-recovered-susceptible (SIRS) model and the ensemble adjustment Kalman filter (EAKF). Model performance was assessed by sensitivity and specificity in predicting epidemics, and by accuracies in predicting peak timing and magnitude.

Results

Our forecast system can generally well predict seasonal influenza epidemics (mean sensitivity>87.5%; mean specificity >80%). The average forecast accuracies were 82% and 60% for peak timing and magnitude at 3–6 weeks ahead for northern China, higher than those of 42% and 20% for southern China. The accuracy was generally better when the forecast was made closer to the actual peak time.

Discussion

The established AH-driven forecasting system can generally well predict the occurrence of seasonal influenza epidemics in China.

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a real-time quantification of the effects of relaxing countermeasures.

Background

This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027.

Methods

Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years.

Results

Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30–39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027.

Conclusions

Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.

With the emergence of SARS-CoV-2, various non-pharmaceutical interventions were adopted to control virus transmission, including school closures. Subsequently, the introduction of vaccines mitigated not only disease severity but also the spread of SARS-CoV-2. This study leveraged an adapted SIR model and non-linear mixed-effects modeling to quantify the impact of remote learning, school holidays, the emergence of Variants of Concern (VOCs), and the role of vaccinations in controlling SARS-CoV-2 spread across 16 German federal states with an age-stratified approach. Findings highlight a significant inverse correlation (Spearman's ρ = −0.92, p < 0.001) between vaccination rates and peak incidence rates across all age groups. Model-parameter estimation using the observed number of cases stratified by federal state and age allowed to assess the effects of school closure and holidays, considering adjustments for vaccinations and spread of VOCs over time. Here, modeling revealed significant (p < 0.001) differences in the virus's spread among pre-school children (0–4), children (5–11), adolescents (12–17), adults (18–59), and the elderly (60+). The transition to remote learning emerged as a critical measure in significantly reducing infection rates among children and adolescents (p < 0.001), whereas an increased infection risk was noted among the elderly during these periods, suggesting a shift in infection networks due to altered caregiving roles. Conversely, during school holiday periods, infection rates among adolescents mirrored those observed when schools were open. Simulation exercises based on the model provided evidence that COVID-19 vaccinations might serve a dual purpose: they protect the vaccinated individuals and contribute to the broader community's safety.

During an epidemic, such as the COVID-19 pandemic, policy-makers are faced with the decision of implementing effective, yet socioeconomically costly intervention strategies, such as school and workplace closure, physical distancing, etc. In this study, we propose a rigorous definition of epidemiological strategies. In addition, we develop a scheme for comparing certain epidemiological strategies, with the goal of providing policy-makers with a tool for their systematic comparison. Then, we put the suggested scheme to the test by employing an age-based epidemiological compartment model introduced in Bitsouni et al. (2024), coupled with data from the literature, in order to compare the effectiveness of age-based and horizontal interventions. In general, our findings suggest that these two are comparable, mainly at a low or medium level of intensity.