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Coffee berry disease (CBD), resulting from the Colletotrichum kahawae fungal pathogen, poses a severe risk to coffee crops worldwide. Focused on coffee berries, it triggers substantial economic losses in regions relying heavily on coffee cultivation. The devastating impact extends beyond agricultural losses, affecting livelihoods and trade economies. Experimental insights into coffee berry disease provide crucial information on its pathogenesis, progression, and potential mitigation strategies for control, offering valuable knowledge to safeguard the global coffee industry. In this paper, we investigated the mathematical model of coffee berry disease, with a focus on the dynamics of the coffee plant and Colletotrichum kahawae pathogen populations, categorized as susceptible, exposed, infected, pathogenic, and recovered (SEIPR) individuals. To address the system of nonlinear differential equations and obtain semi-analytical solution for the coffee berry disease model, a novel analytical approach combining the Shehu transformation, Akbari – Ganji, and Pade approximation method (SAGPM) was utilized. A comparison of analytical results with numerical simulations demonstrates that the novel SAGPM is excellent efficiency and accuracy. Furthermore, the sensitivity analysis of the coffee berry disease model examines the effects of all parameters on the basic reproduction number $R_0$. Moreover, in order to examine the behavior of the model individuals, we varied some parameters in CBD. Through this analysis, we obtained valuable insights into the responses of the coffee berry disease model under various conditions and scenarios. This research offers valuable insights into the utilization of SAGPM and sensitivity analysis for analyzing epidemiological models, providing significant utility for researchers in the field.

Eco-epidemiological models provide insights into factors influencing disease transmission and host population stability. This study developed two eco-epidemiological models to investigate the impacts of prey refuge availability and an Allee effect on dynamics. Model A incorporated these mechanisms, while model B did not. Both models featured predator – prey and disease transmission and were analyzed mathematically and via simulation. Model equilibrium states were examined locally and globally under differing parameter combinations representative of environmental scenarios. Model A and B demonstrated globally stable conditions within certain parameter ranges, signalling refuge and Allee effect terms promote robustness. Moreover, model A showed a higher potential toward extinction of the species as a result of incorporating the Allee effect. Bifurcation analyses revealed qualitative shifts in behavior triggered by modifications like altered predation mortality. Model A manifested a transcritical bifurcation indicating critical population thresholds. Additional bifurcation types were noticed when refuge and Allee stabilizing impacts were absent in model B. Findings showed disease crowding effect and that host persistence is positively associated with refuge habitat, reducing predator – prey encounters. The Allee effect also calibrated stability via heightened sensitivity to small groups. Simulations aligned with mathematical predictions. Model A underwent bifurcations at critical predator death rates impacting prey outcomes. This work provides a valuable framework to minimize transmission given resource availability or demographic alterations, generating testable hypotheses.

The article deals with the development of a methodological approach to forecasting and modeling the socioeconomic consequences of viral epidemics in conditions of heterogeneous economic development of territorial systems. The relevance of the research stems from the need for rapid mechanisms of public management and stabilization of adverse epidemiological situation, taking into account the spatial heterogeneity of the spread of COVID-19, accompanied by a concentration of infection in large metropolitan areas and territories with high economic activity. The aim of the work is to substantiate a methodology to assess the spatial heterogeneity of the spread of coronavirus infection, find poles of its growth, emerging spatial clusters and zones of their influence with the assessment of inter-territorial relationships, as well as simulate the effects of worsening epidemiological situation on the dynamics of economic development of regional systems. The peculiarity of the developed approach is the spatial clustering of regional systems by the level of COVID-19 incidence, conducted using global and local spatial autocorrelation indices, various spatial weight matrices, and L.Anselin mutual influence matrix based on the statistical information of the Russian Federal State Statistics Service. The study revealed a spatial cluster characterized by high levels of infection with COVID-19 with a strong zone of influence and stable interregional relationships with surrounding regions, as well as formed growth poles which are potential poles of further spread of coronavirus infection. Regression analysis using panel data not only confirmed the impact of COVID-19 incidence on the average number of employees in enterprises, the level of average monthly nominal wages, but also allowed to form a model for scenario prediction of the consequences of the spread of coronavirus infection. The results of this study can be used to form mechanisms to contain the coronavirus infection and stabilize socio-economic at macroeconomic and regional level and restore the economy of territorial systems, depending on the depth of the spread of infection and the level of economic damage caused.

Epidemics severely destabilize economies by reducing productivity, weakening consumer spending, and overwhelming public infrastructure, often culminating in economic recessions. The COVID-19 pandemic underscored the critical role of nonpharmaceutical interventions, such as lockdowns, in containing infectious disease transmission. This study investigates how the progression of epidemics and the implementation of lockdown policies shape the economic well-being of populations. By integrating compartmental ordinary differential equation (ODE) models, the research analyzes the interplay between epidemic dynamics and economic outcomes, particularly focusing on how varying lockdown intensities influence both disease spread and population wealth. Findings reveal that epidemics inflict significant economic damage, but timely and stringent lockdowns can mitigate healthcare system overload by sharply reducing infection peaks and delaying the epidemic’s trajectory. However, carefully timed lockdown relaxation is equally vital to prevent resurgent outbreaks. The study identifies key epidemiological thresholds—such as transmission rates, recovery rates, and the basic reproduction number $(\mathfrak{R}0)$ — that determine the effectiveness of lockdowns. Analytically, it pinpoints the optimal proportion of isolated individuals required to minimize total infections in scenarios where permanent immunity is assumed. Economically, the analysis quantifies lockdown impacts by tracking population wealth, demonstrating that economic outcomes depend heavily on the fraction of isolated individuals who remain economically productive. Higher proportions of productive individuals during lockdowns correlate with better wealth retention, even under fixed epidemic conditions. These insights equip policymakers with actionable frameworks to design balanced lockdown strategies that curb disease spread while safeguarding economic stability during future health crises.

Acute respiratory infections are a major public health concern because they are the leading cause of illness and death in many countries. Therefore, there is great interest in developing models and methods capable of modeling the spread of these infections within communities, with the aim of controlling outbreaks and preventing their spread. Agent-based models (ABM) are one of the most important tools in epidemiological research for modeling epidemic dynamics in realistic populations, but they face significant challenges in terms of computational complexity in their operation and calibration of epidemiological data, as parameter estimation typically requires repeated simulations across large parameter spaces to determine plausible values for key epidemiological parameters. This paper addresses the problem of alleviating computational constraints in the inverse problem of calibrating an ABM model for simulating the spread of respiratory infections in Saint Petersburg. The paper proposes the application of machine learning surrogate to link epidemic trajectories to underlying epidemiological parameters, enabling them to quickly infer parameter estimates from observed epidemic data. This is done by formulating the task of calibrating ABMs against epidemiological data as a supervised learning problem, where sequences extracted from epidemiological trajectories are associated with underlying epidemiological parameters. The research was based on evaluating the performance of attention-based sequence modeling, probabilistic deep learning, and distributional regression for inferring parameter estimates from truncated sequences of epidemic trajectories. Experimental evaluations have demonstrated the effectiveness of this approach and its practical and straightforward application. The results also indicated the superiority of attention-based sequence modeling, as it showed more consistent performance across metrics and horizons in accurate parameter estimation and credible uncertainty quantification. Distributional regression modeling also showed good performance with specific strengths in point accuracy while probabilistic deep learning performed poorly, especially at longer input horizons.