Infection-induced increases to population size during cycles in a discrete-time epidemic model.

One-dimensional discrete-time population models, such as those that involve Logistic or Ricker growth, can exhibit periodic and chaotic dynamics. Expanding the system by one dimension to incorporate epidemiological interactions causes an interesting complexity of new behaviors. Here, we examine a discrete-time two-dimensional susceptible-infectious (SI) model with Ricker growth and show that the introduction of infection can not only produce a distinctly different bifurcation structure than that of the underlying disease-free system but also lead to counter-intuitive increases in population size. We use numerical bifurcation analysis to determine the influence of infection on the location and types of bifurcations. In addition, we examine the appearance and extent of a phenomenon known as the 'hydra effect,' i.e., increases in total population size when factors, such as mortality, that act negatively on a population, are increased. Previous work, primarily focused on dynamics at fixed points, showed that the introduction of infection that reduces fecundity to the SI model can lead to a so-called 'infection-induced hydra effect.' Our work shows that even in such a simple two-dimensional SI model, the introduction of infection that alters fecundity or mortality can produce dynamics can lead to the appearance of a hydra effect, particularly when the disease-free population is at a cycle.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

A study of the attenuation stage of a global infectious disease.

Introduction: Differences in control measures and response speeds between regions may be responsible for the differences in the number of infections of global infectious diseases. Therefore, this article aims to examine the decay stage of global infectious diseases. We demonstrate our method by considering the first wave of the COVID-19 epidemic in 2020. Methods: We introduce the concept of the attenuation rate into the varying coefficient SEIR model to measure the effect of different cities on epidemic control, and make inferences through the integrated adjusted Kalman filter algorithm. Results: We applied the varying coefficient SEIR model to 136 cities in China where the total number of confirmed cases exceeded 20 after the implementation of control measures and analyzed the relationship between the estimated attenuation rate and local factors. Subsequent analysis and inference results show that the attenuation rate is significantly related to the local annual GDP and the longitude and latitude of a city or a region. We also apply the varying coefficient SEIR model to other regions outside China. We find that the fitting curve of the average daily number of new confirmed cases simulated by the variable coefficient SEIR model is consistent with the real data. Discussion: The results show that the cities with better economic development are able to control the epidemic more effectively to a certain extent. On the other hand, geographical location also affected the effectiveness of regional epidemic control. In addition, through the results of attenuation rate analysis, we conclude that China and South Korea have achieved good results in controlling the epidemic in 2020.

An immuno-epidemiological model with waning immunity after infection or vaccination.

In epidemics, waning immunity is common after infection or vaccination of individuals. Immunity levels are highly heterogeneous and dynamic. This work presents an immuno-epidemiological model that captures the fundamental dynamic features of immunity acquisition and wane after infection or vaccination and analyzes mathematically its dynamical properties. The model consists of a system of first order partial differential equations, involving nonlinear integral terms and different transfer velocities. Structurally, the equation may be interpreted as a Fokker-Planck equation for a piecewise deterministic process. However, unlike the usual models, our equation involves nonlocal effects, representing the infectivity of the whole environment. This, together with the presence of different transfer velocities, makes the proved existence of a solution novel and nontrivial. In addition, the asymptotic behavior of the model is analyzed based on the obtained qualitative properties of the solution. An optimal control problem with objective function including the total number of deaths and costs of vaccination is explored. Numerical results describe the dynamic relationship between contact rates and optimal solutions. The approach can contribute to the understanding of the dynamics of immune responses at population level and may guide public health policies.

Spatiotemporal dynamics of cholera epidemics in Ethiopia: 2015-2021.

Since the onset of the seventh cholera pandemic, Ethiopia has been affected by recurrent epidemics. However, the epidemiology of cholera in this country remains poorly understood. This study aimed to describe cholera outbreak characteristics in Ethiopia from 2015 to 2021. During this period, Ethiopia experienced four epidemic waves. The first wave involved nationwide outbreaks during the second half of 2016 followed by outbreaks predominantly affecting Somali Region in 2017. The second wave primarily affected Tigray and Afar Regions. During the third wave, multiple smaller-scale outbreaks occurred during 2019. The fourth wave was limited to Bale Zone (Oromia Region) in 2021. Overall, a north to south shift was observed over the course of the study period. Major cholera transmission factors included limited access to safe water and sanitation facilities. Severe weather events (drought and flooding) appear to aggravate cholera diffusion. Cholera transmission between Ethiopia and nearby countries (Kenya and Somalia), likely plays a major role in regional cholera dynamics. Overall, this study provides the first understanding of recent spatiotemporal cholera dynamics in Ethiopia to inform cholera control and elimination strategies.

Computational and stability analysis of Ebola virus epidemic model with piecewise hybrid fractional operator.

In this manuscript, we developed a nonlinear fractional order Ebola virus with a novel piecewise hybrid technique to observe the dynamical transmission having eight compartments. The existence and uniqueness of a solution of piecewise derivative is treated for a system with Arzel'a-Ascoli and Schauder conditions. We investigate the effects of classical and modified fractional calculus operators, specifically the classical Caputo piecewise operator, on the behavior of the model. A model shows that a completely continuous operator is uniformly continuous, and bounded according to the equilibrium points. The reproductive number R0 is derived for the biological feasibility of the model with sensitivity analysis with different parameters impact on the model. Sensitivity analysis is an essential tool for comprehending how various model parameters affect the spread of illness. Through a methodical manipulation of important parameters and an assessment of their impact on Ro, we are able to learn more about the resiliency and susceptibility of the model. Local stability is established with next Matignon method and global stability is conducted with the Lyapunov function for a feasible solution of the proposed model. In the end, a numerical solution is derived with Newton's polynomial technique for a piecewise Caputo operator through simulations of the compartments at various fractional orders by using real data. Our findings highlight the importance of fractional operators in enhancing the accuracy of the model in capturing the intricate dynamics of the disease. This research contributes to a deeper understanding of Ebola virus dynamics and provides valuable insights for improving disease modeling and public health strategies.

HIV Care Continuum and Preexposure Prophylaxis Program in Federal Bureau of Prisons, United States.

In 2019, the US Department of Health and Human Services launched the Ending the HIV Epidemic in the US initiative (EHE) with the goal of reducing new HIV infections by 90% by 2030. This initiative identifies 4 pillars (diagnose, treat, prevent, and respond) to address the HIV epidemic in the United States. To advance the EHE goals, the Federal Bureau of Prisons (FBOP) has implemented interventions at all points of the HIV care continuum. The FBOP has addressed the EHE pillar of prevention through implementing preexposure prophylaxis, developing a strategy to decrease the risk of new HIV infection, and providing guidance to FBOP healthcare providers. This article describes the implementation of programs to improve the HIV care continuum and end the epidemic of HIV within the FBOP including a review of methodology to implement an HIV preexposure prophylaxis program.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19.

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

Impact of immune evasion, waning and boosting on dynamics of population mixing between a vaccinated majority and unvaccinated minority.

BACKGROUND: We previously demonstrated that when vaccines prevent infection, the dynamics of mixing between vaccinated and unvaccinated sub-populations is such that use of imperfect vaccines markedly decreases risk for vaccinated people, and for the population overall. Risks to vaccinated people accrue disproportionately from contact with unvaccinated people. In the context of the emergence of Omicron SARS-CoV-2 and evolving understanding of SARS-CoV-2 epidemiology, we updated our analysis to evaluate whether our earlier conclusions remained valid. METHODS: We modified a previously published Susceptible-Infectious-Recovered (SIR) compartmental model of SARS-CoV-2 with two connected sub-populations: vaccinated and unvaccinated, with non-random mixing between groups. Our expanded model incorporates diminished vaccine efficacy for preventing infection with the emergence of Omicron SARS-CoV-2 variants, waning immunity, the impact of prior immune experience on infectivity, "hybrid" effects of infection in previously vaccinated individuals, and booster vaccination. We evaluated the dynamics of an epidemic within each subgroup and in the overall population over a 10-year time horizon. RESULTS: Even with vaccine efficacy as low as 20%, and in the presence of waning immunity, the incidence of COVID-19 in the vaccinated subpopulation was lower than that among the unvaccinated population across the full 10-year time horizon. The cumulative risk of infection was 3-4 fold higher among unvaccinated people than among vaccinated people, and unvaccinated people contributed to infection risk among vaccinated individuals at twice the rate that would have been expected based on the frequency of contacts. These findings were robust across a range of assumptions around the rate of waning immunity, the impact of "hybrid immunity", frequency of boosting, and the impact of prior infection on infectivity in unvaccinated people. INTERPRETATION: Although the emergence of the Omicron variants of SARS-CoV-2 has diminished the protective effects of vaccination against infection with SARS-CoV-2, updating our earlier model to incorporate loss of immunity, diminished vaccine efficacy and a longer time horizon, does not qualitatively change our earlier conclusions. Vaccination against SARS-CoV-2 continues to diminish the risk of infection among vaccinated people and in the population as a whole. By contrast, the risk of infection among vaccinated people accrues disproportionately from contact with unvaccinated people.

Exploring local and global stability of COVID-19 through numerical schemes.

Respiratory sensitivity and pneumonia are possible outcomes of the coronavirus (COVID-19). Surface characteristics like temperature and sunshine affect how long the virus survives. This research article analyzes COVID-19 mathematical model behavior based on symptomatic and non-symptomatic individuals. In the reproductive model, the best result indicates the intensity of the epidemic. Our model remained stable at a certain point under controlled conditions after we evaluated a specific element. This approach is in place of traditional approaches such as Euler's and Runge-Kutta's. An unusual numerical approach known as the non-standard finite difference (NSFD) scheme is used in this article. This numerical approach gives us positivity. A dependable numerical analysis allowed us to evaluate different approaches and verify our theoretical results. Unlike the widely used Euler and RK4 approaches, we investigated the benefits of implementing NSFD schemes. By numerically simulating COVID-19 in a variety of scenarios, we demonstrated how our theoretical concepts work. The simulation findings support the usefulness of both approaches.

Concept analysis of health system resilience.

BACKGROUND: There are several definitions of resilience in health systems, many of which share some characteristics, but no agreed-upon framework is universally accepted. Here, we review the concept of resilience, identifying its definitions, attributes, antecedents and consequences, and present the findings of a concept analysis of health system resilience. METHODS: We follow Schwarz-Barcott and Kim's hybrid model, which consists of three phases: theoretical, fieldwork and final analysis. We identified the concept definitions, attributes, antecedents and consequences of health system resilience and constructed an evidence-informed framework on the basis of the findings of this review. We searched PubMed, PsycINFO, CINAHL Complete, EBSCOhost-Academic Search and Premier databases and downloaded identified titles and abstracts on Covidence. We screened 3357 titles and removed duplicate and ineligible records; two reviewers then screened each title, and disagreements were resolved by discussion with the third reviewer. From the 130 eligible manuscripts, we identified the definitions, attributes, antecedents and consequences using a pre-defined data extraction form. RESULTS: Resilience antecedents are decentralization, available funds, investments and resources, staff environment and motivation, integration and networking and finally, diversification of staff. The attributes are the availability of resources and funds, adaptive capacity, transformative capacity, learning and advocacy and progressive leadership. The consequences of health system resilience are improved health system performance, a balanced governance structure, improved expenditure and financial management of health and maintenance of health services that support universal health coverage (UHC) throughout crises. CONCLUSION: A resilient health system maintains quality healthcare through times of crisis. During the coronavirus disease 2019 (COVID-19) epidemic, several seemingly robust health systems were strained under the increased demand, and services were disrupted. As such, elements of resilience should be integrated into the functions of a health system to ensure standardized and consistent service quality and delivery. We offer a systematic, evidence-informed method for identifying the attributes of health system resilience, intending to eventually be used to develop a measuring tool to evaluate a country's health system resilience performance.

From erosion to epidemics: Understanding the overlapping vulnerability of hydrogeomorphic hotspots, malaria affliction, and poverty in Nigeria.

Hydrogeomorphic changes, encompassing erosion, waterlogging, and siltation, disproportionately threaten impoverished rural communities. Yet, they are often marginalized in discussions of disasters. This oversight is especially concerning as vulnerable households with limited healthcare access are most susceptible to related diseases and displacement. However, our current understanding of how these risks intersect remains limited. We explore the complex relationships between hydrogeomorphic hazards, malaria incidence, and poverty in Nigeria. Through spatial analyses we expand traditional boundaries, incorporating factors such as healthcare access, migration patterns, dam locations, demographics, and wealth disparities into a unified framework. Our findings reveal a stark reality: most residents in hydrogeomorphic hotspots live in poverty (earnings per person ≤$1.25/day), face elevated malaria risks (80 % in malaria hotspots), reside near dams (59 %), and struggle with limited healthcare access. Moreover, exposure to hydrogeomorphic hotspots could double by 2080, affecting an estimated 5.8 million Nigerians. This forecast underscores the urgent need for increased support and targeted interventions to protect those living in poverty within these hazardous regions. In shedding light on these dynamics, we expose and emphasise the pressing urgency of the risks borne by the most vulnerable populations residing in these regions-communities often characterised by limited wealth and resilience.

Modelling the impact of hybrid immunity on future COVID-19 epidemic waves.

BACKGROUND: Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves. METHODS: To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region. RESULTS: We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants. CONCLUSIONS: Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

["God for us!": Catholic festivals and rites in times of epidemics, Salvador-BA, Brasil, 1918-1919]. / "Valha-nos a Providência!": festas e ritos católicos em tempos epidêmicos, Salvador-BA, Brasil, 1918-1919.

This article analyzes the reactions of Catholics linked to lay associations in the city of Salvador, in the period of the Spanish flu (1918) and smallpox (1919). Newspapers were the main sources used to identify the festivals and rites, both those practiced to ask for the intercession of the saints, and those that were suspended due to the need for social isolation. In spite of both diseases being transmissible and the short interval between the two epidemics, the analysis of the sources showed different reactions from the faithful regarding the measures of protection and the search for a cure.

O artigo analisa as reações dos católicos vinculados às associações leigas na cidade do Salvador, no período da gripe espanhola (1918) e da varíola (1919). Os jornais foram as principais fontes utilizadas para a identificação das festas e dos ritos, tanto dos praticados para pedir a intercessão dos santos quanto daqueles que foram suspensos em função da necessidade de isolamento social. Apesar de ambas as doenças serem transmissíveis e do curto espaço de tempo entre as duas epidemias, a análise das fontes evidenciou diferentes reações dos fiéis quanto às medidas de proteção e busca da cura.

Investigating and analyzing the current situation and factors influencing chronic neck, shoulder, and lumbar back pain among medical personnel after the epidemic.

BACKGROUND: Chronic shoulder and neck pain is one of the most common chronic occupational disorders, with an average incidence rate of 48.5%, severely affecting patients' quality of life and ability to work. According to epidemiological research, the prevalence of chronic neck, shoulder, and low back pain in adults over the age of 45 ranges from 40 to 80%. According to reports, medical staff have a higher incidence rate than other populations, and there is a positive correlation between the grade of the medical institution and the incidence rate, making medical staff a priority group for the prevention of chronic neck, shoulder, and low back pain. By the end of 2022, China has been fully opened to epidemic prevention and control, the total number of patients in domestic hospitals has increased significantly, and resulting in medical personnel shoulting great pressure, which seriously affects the physical and mental health of medical personnel. The aim of this study was to explore the risk factors of chronic neck, shoulder and lumbar back pain in medical staff. To provide guidelines for medical staff to improve cervical and lumbar subacute pain and reduce the emergence of spinal lesions. METHODS: From January to February 2023, 602 staff members of a third-grade hospital in Zunyi City were studied by Questionnaire star. Univariate and multivariate Logistic regression were used to analyze the independent risk factors of chronic neck, shoulder and lumbar back pain in medical staff, with stepwise regression utilized to choose the optimum model. The model was selected using Akaike's information criterion (AIC) and the Hosmer-Lemeshow goodness-of-fit test. RESULTS: A total of 602 medical staff were polled, and the findings revealed that 588 cases of chronic neck, shoulder, and low back pain of varied severity had occurred in the previous 1 to 2 years, with a 97.7% incidence rate; logistic regression analysis revealed that anxiety level, frequency of bending over in the previous 1 to 2 years, whether related preventive measures were taken at work, gender, positive senior title, daily ambulation time, and whether the department they worked in organized independent influencing factors. CONCLUSION: The incidence of chronic neck, shoulder, and lumbar back pain among medical staff is high; its influencing factors are different and have not been systematically identified. Hospitals should take effective measures tailored to local conditions to improve the physical and mental health of medical staff.

Changes in the endemic-epidemic pattern of malaria in Colombia, 1978-2021.

BACKGROUND: Malaria is a major global public health issue with varying epidemiologies across countries. In Colombia, it is a priority endemic-epidemic event included in the national public health policy. However, evidence demonstrating nationwide variations in the disease behavior is limited. This study aimed to analyze changes in the levels and distribution of endemic-epidemic malaria transmission in the eco-epidemiological regions of Colombia from 1978 to 1999 and 2000 to 2021. METHODS: We conducted a comprehensive time-series study using official secondary data on malaria-associated morbidity and mortality in Colombia from 1978 to 2021. Temporal-spatial and population variables were analyzed, and the absolute and relative frequency measures of general and regional morbidity and mortality were estimated. RESULTS: We observed an 18% reduction in malaria endemic cases between the two study periods. The frequency and severity of the epidemic transmission of malaria varied less and were comparable across both periods. A shift was observed in the frequency of parasitic infections, with a tendency to match and increase infections by Plasmodium falciparum. The risk of malaria transmission varied significantly among the eco-epidemiological regions during both study periods. This study demonstrated a sustained decrease of 78% in malarial mortality. CONCLUSIONS: Although the endemic components of malaria decreased slightly between the two study periods, the epidemic pattern persisted. There were significant variations in the risk of transmission across the different eco-epidemiological regions. These findings underscore the importance of targeted public health interventions in reducing malarial morbidity and mortality rates in Colombia.

Fractional epidemic model of coronavirus disease with vaccination and crowding effects.

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh-Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if R 0 < 1 and endemic equilibrium is locally asymptotically stable if R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if R 0 < 1 and endemic equilibrium is globally asymptotically stable if R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks.