Disease Transmission Dynamics Research Articles

Mathematical Modeling of the Transmission Dynamics of Gumboro Disease

Gumboro disease is a viral poultry disease that causes immune suppression on the infected birds leading to poor production, mortality, and exposure to secondary infections, hence a major threat in the poultry industry worldwide. A mathematical model of the transmission dynamics of Gumboro disease is developed in this paper having four compartments of chicken population and one compartment of Gumboro pathogen population. The basic reproduction number Rog is derived, and the dynamical behaviors of both the disease-free equilibrium (DFE) and endemic equilibrium are analyzed using the ordinary differential equation theory. From the analysis, we found that the system exhibits an asymptotic stable DFE whenever Rog<1 and an asymptotic stable EE whenever Rog>1. The numerical simulation to verify the theoretical results was carried out using MATLAB ode45 solver, and the results were found to be consistent with the theoretical findings.

A model for the control of transmission dynamics of human monkeypox disease in Sub-Saharan Africa

The Human Monkeypox virus has received significant research interest in recent times. While few researchers have studied the effects of vaccination on human-to-animal or animal-to-human Monkeypox transmission, others just studied the effects of treatment on human Monkeypox. In this article, we made the proposition of a deterministic vaccine model that deals with the dynamics of the effects of vaccination and treatment on human Monkeypox in sub-Saharan Africa. We investigated the effects of vaccination on the various epidemiological classes qualitatively. The findings from the analysis of the model are that the model possesses two equilibria, locally asymptotically stable disease-free equilibrium (DFE) when an epidemiological threshold - the effective reproductive number is less than unity, and locally asymptotically stable endemic equilibrium when the number is greater than unity. We then corroborated the theoretical findings with numerical simulations, which reveal that when the rate of vaccination is increased resulting in many newly born persons in the populace being vaccinated, the prevalence of the deadly scourge is significantly reduced, while newly born individuals that miss vaccination experience a drastic torment of the deadly disease that often occasion death. Further revelation from the simulations is that when greater efforts

Linking Spontaneous Behavioral Changes to Disease Transmission Dynamics: Behavior Change Includes Periodic Oscillation.

Behavior change significantly influences the transmission of diseases during outbreaks. To incorporate spontaneous preventive measures, we propose a model that integrates behavior change with disease transmission. The model represents behavior change through an imitation process, wherein players exclusively adopt the behavior associated with higher payoff. We find that relying solely on spontaneous behavior change is insufficient for eradicating the disease. The dynamics of behavior change are contingent on the basic reproduction number corresponding to the scenario where all players adopt non-pharmaceutical interventions (NPIs). When , partial adherence to NPIs remains consistently feasible. We can ensure that the disease stays at a low level or maintains minor fluctuations around a lower value by increasing sensitivity to perceived infection. In cases where oscillations occur, a further reduction in the maximum prevalence of infection over a cycle can be achieved by increasing the rate of behavior change. When , almost all players consistently adopt NPIs if they are highly sensitive to perceived infection. Further consideration of saturated recovery leads to saddle-node homoclinic and Bogdanov-Takens bifurcations, emphasizing the adverse impact of limited medical resources on controlling the scale of infection. Finally, we parameterize our model with COVID-19 data and Tokyo subway ridership, enabling us to illustrate the disease spread co-evolving with behavior change dynamics. We further demonstrate that an increase in sensitivity to perceived infection can accelerate the peak time and reduce the peak size of infection prevalence in the initial wave.

Diet-induced obesity affects influenza disease severity and transmission dynamics in ferrets.

Obesity, and the associated metabolic syndrome, is a risk factor for increased disease severity with a variety of infectious agents, including influenza virus. Yet, the mechanisms are only partially understood. As the number of people, particularly children, living with obesity continues to rise, it is critical to understand the role of host status on disease pathogenesis. In these studies, we use a diet-induced obese ferret model and tools to demonstrate that, like humans, obesity resulted in notable changes to the lung microenvironment, leading to increased clinical disease and viral spread to the lower respiratory tract. The decreased antiviral responses also resulted in obese animals shedding higher infectious virus for a longer period, making them more likely to transmit to contacts. These data suggest that the obese ferret model may be crucial to understanding obesity's impact on influenza disease severity and community transmission and a key tool for therapeutic and intervention development for this high-risk population.

Dynamics analysis and optimal control of delayed SEIR model in COVID-19 epidemic

The coronavirus disease 2019 (COVID-19) remains serious around the world and causes huge deaths and economic losses. Understanding the transmission dynamics of diseases and providing effective control strategies play important roles in the prevention of epidemic diseases. In this paper, to investigate the effect of delays on the transmission of COVID-19, we propose a delayed SEIR model to describe COVID-19 virus transmission, where two delays indicating the incubation and recovery periods are introduced. For this system, we prove its solutions are nonnegative and ultimately bounded with the nonnegative initial conditions. Furthermore, we calculate the disease-free and endemic equilibrium points and analyze the asymptotical stability and the existence of Hopf bifurcations at these equilibrium points. Then, by taking the weighted sum of the opposite number of recovered individuals at the terminal time, the number of exposed and infected individuals during the time horizon, and the system cost of control measures as the cost function, we present a delay optimal control problem, where two controls represent the social contact and the pharmaceutical intervention. Necessary optimality conditions of this optimal control problem are exploited to characterize the optimal control strategies. Finally, numerical simulations are performed to verify the theoretical analysis of the stability and Hopf bifurcations at the equilibrium points and to illustrate the effectiveness of the obtained optimal strategies in controlling the COVID-19 epidemic.

Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic, along with the implementation of public health and social measures (PHSMs), have markedly reshaped infectious disease transmission dynamics. We analysed the impact of PHSMs on 24 notifiable infectious diseases (NIDs) in the Chinese mainland, using time series models to forecast transmission trends without PHSMs or pandemic. Our findings revealed distinct seasonal patterns in NID incidence, with respiratory diseases showing the greatest response to PHSMs, while bloodborne and sexually transmitted diseases responded more moderately. 8 NIDs were identified as susceptible to PHSMs, including hand, foot, and mouth disease, dengue fever, rubella, scarlet fever, pertussis, mumps, malaria, and Japanese encephalitis. The termination of PHSMs did not cause NIDs resurgence immediately, except for pertussis, which experienced its highest peak in December 2023 since January 2008. Our findings highlight the varied impact of PHSMs on different NIDs and the importance of sustainable, long-term strategies, like vaccine development.

Genetic Structure of Introduced Plasmodium vivax Malaria Isolates in Greece, 2015–2019

Greece has been malaria-free since 1974, after an intense malaria control program. However, as Greece hosts migrant populations from P. vivax malaria-endemic countries, there is a risk of introducing the disease to specific vulnerable and receptive areas of the country. Knowledge of the genetic diversity of P. vivax populations is essential for understanding the dynamics of malaria disease transmission in a given region. We used nine highly polymorphic markers to genotype 124 P. vivax-infected archived DNA samples from human blood specimens referred to the NMRL from all over Greece throughout 2015–2019. The genotypic variability of the samples studied was noted, as they comprised several unique haplotypes, indicative of the importation of a large number of different P. vivax strains in the country. However, only a few events of local transmission were recorded. Genotyping revealed and confirmed the same clusters as those identified through epidemiological investigation. In only one introduction event was the index case found. No sustained/ongoing malaria transmissions in/between the studied regions or during consecutive years or additional foci of local transmission were observed. Genotyping is an important component in assisting malaria surveillance, as it provides information concerning the patterns of introduction and the effectiveness of implemented malaria control and elimination measures.

Neural Network Approach for Cholera Dynamics: Integrating Deterministic and Stochastic Insights

In this article, a comprehensive qualitative analysis framework is presented that integrates stochastic and deterministic models to explore the transmission dynamics of Cholera disease. The significance of the reproduction number in understanding the spread of the disease and in formulating effective control strategies is emphasized. The sensitivity analysis of model parameters is conducted to identify those that critically influence transmission. By converting the deterministic model into a stochastic form, Brownian motion and stochastic parameters are incorporated to capture the inherent randomness in disease dynamics. This approach is further enriched by the application of neural networks (NN) to simulate and validate the complex interactions involved in Cholera transmission. The analysis extends to the global positive solution, stochastic reproduction number, and virus-free stochastic dynamics, providing an improved understanding of the disease's behavior. The critical conditions necessary for the extinction and long-term elimination of the virus are also addressed, highlighting the importance of controlling its spread. The stationary distribution of the disease is examined through the lens of a regular Markov process, enhancing the understanding of the stochastic model's dynamics. The results verified by neural networks, compared with those obtained from conventional numerical methods, underscore the accuracy and versatility of neural networks in epidemiological research, offering new perspectives and methodologies for tackling this global health challenge.

Threshold dynamics of a reaction–advection–diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction–advection–diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} and show that the disease-free periodic solution is globally attractive when R0<1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0<1$$\\end{document} whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R0>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0>1$$\\end{document}. Moreover, we find that R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Trypanosoma cruzi in domestic and wild mammals in the northeast region of Colombia

Trypanosoma cruzi hosts can serve as a source of infection for animals, vectors, and humans, contributing to the establishment of Chagas disease (CD) in a given area. Traditionally, the Department of Córdoba has not been considered a transmission area for CD; however, the report of several acute cases of Chagas disease highlights the importance of studying the dynamics of disease transmission in this region. This study aimed to detect T. cruzi in domestic and wild mammals in the department of Córdoba. In 2017, a cross-sectional descriptive study was conducted in six villages in two municipalities in the department of Córdoba. Blood samples from dogs living in the zones were collected in EDTA vacutainer tubes for domestic mammals. Wild mammals were collected using Sherman and Tomahawk traps and mist nets in crops and peridomiciles. T. cruzi DNA was detected using the kinetoplast DNA (kDNA) variable region and the tandem repeat satellite region of T. cruzi as molecular targets. We sampled 168 dogs and 146 wild mammals. The detected prevalence of T. cruzi was 6.37%; the TcI lineage was found in D. marsupialis, H. anomalus, and one canine. A specimen of D. marsupialis with TcI and TcII lineages was also identified. T. cruzi DNA was detected in domestic and wild animals in the study area, indicating the circulation of the parasite in peridomestic environments. D. marsupialis may represent an important host in maintaining this region's wild and domestic cycle.

Using a Dynamic Model to Estimate the Cost-Effectiveness of HPV Vaccination in Iran.

This study aimed to determine the cost-effectiveness of vaccination against HPV. An age-sex structured dynamic disease transmission model was created to estimate the spread of HPV and the HPV-related incidence of cervical cancer (CC) in Iran. Sixteen age groups of men and women were incorporated to reflect the differences in sexual preferences, vaccination uptake, and disease-related outcomes. Three scenarios were evaluated by using an Incremental Cost-Effectiveness Ratio (ICER) with gained quality-adjusted life years (QALYs). ICER values below one gross domestic product (GDP) per capita are evaluated as highly cost-effective. Vaccination reduces the number of infections and CC-related mortality. Over time, the vaccinated group ages and older age groups experience protection. An initial investment is required and savings in treatment spending reduce the impact over time. Vaccinating girls only was found to be cost-effective, with an ICER close to once the GDP per capita. Vaccinating both sexes was shown to be less cost-effective compared to girls only, and vaccinating boys only was not found to be cost-effective, with an ICER between once and three times, and greater than three times the GDP per capita, respectively. The estimates are conservative since societal cost-saving and the impact of other HPV-related illnesses were not considered and would likely reduce the ICERs.

Combining wastewater surveillance and case data in estimating the time-varying effective reproduction number

Wastewater surveillance has been increasingly acknowledged as a useful tool for monitoring transmission dynamics of infections of public health concern, including the coronavirus disease (COVID-19). While a range of models have been proposed to estimate the time-varying effective reproduction number (Rt) utilizing clinical data, few have harnessed the viral concentration in wastewater samples to do so, leaving uncertainties about the potential precision gains with its use. In this study, we developed a Bayesian hierarchical model which simultaneously reconstructed the latent infection trajectory and estimated Rt. Focusing on the 2022 and early 2023 COVID-19 transmission trends in Singapore, where mass community wastewater surveillance has become routine, we performed estimations using a spectrum of data sources, including reported case counts, hospital admissions, deaths, and wastewater viral loads. We further explored the performance of our wastewater model across various scenarios with different sampling strategies. The results showed consistent estimates derived from models employing diverse data streams, while models incorporating more wastewater samples exhibited greater uncertainty and variation in the inferred Rts. Additionally, our analysis revealed prominent day-of-the-week effect in reported case counts and substantial temporal variations in ascertainment rates. In response to these findings, we advocate for a hybrid approach leveraging both clinical and wastewater surveillance data to account for changes in case-ascertainment rates. Furthermore, our study demonstrates the possibility of reducing sampling frequency or sample size without compromising estimation accuracy for Rt, highlighting the potential for optimizing resource allocation in surveillance efforts while maintaining robust insights into the transmission dynamics of infectious diseases.

Ensemble learning methods of inference for spatially stratified infectious disease systems.

Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

Microbiome profile of South Korean vector mosquitoes

This research offers a comprehensive exploration of the microbial communities associated with vector mosquitoes from South Korea. Aedes albopictus, Anopheles sinensis, and Culex molestus are vectors of pathogens, and understanding the intricacies of their microbiome profile is paramount for unraveling their roles in disease transmission dynamics. In this study, we characterized the microbiome of the midguts of adult female vector mosquitoes collected from different locations in South Korea. After DNA extraction from dissected mosquito midguts, we used the Illumina MiSeq next-generation sequencing to obtain sequences spanning the V4 hypervariable region of the bacteria 16S rRNA. Morphological and molecular characterization using 506-bp mitochondrial 16S rRNA was used to identify the mosquito species before amplicon sequencing. Across the three vector mosquitoes surveyed, 21 bacteria genera belonging to 20 families and 5 phyla were discovered. Proteobacteria and Bacteriodota were the major phyla of bacteria associated with the three mosquito species. There were significant differences in the gut microbiome genera composition between the species and little variation in the gut microbiome between individuals of the same mosquito species. Wolbachia is the most dominant genus in Aedes while Aeromonas, Acinetobacter, and unassigned taxa are the most common in An. sinensis. In addition to that, Chromobacterium, Chryseobacterium, and Aeromonas are dominant in Cx. molestus. This study sheds light on the complex interactions between mosquitoes and their microbiome, revealing potential implications for vector competence, disease transmission, and vector control strategies.

Modeling the transmission dynamics of a two-strain dengue disease with infection age

In this paper, we introduce a partial differential equation (PDE) model to describe the transmission dynamics of dengue with two viral strains and possible secondary infection for humans. The model features the variable infectiousness during the infectious period, which we call the infection age of the infectious host. We define two thresholds [Formula: see text] and [Formula: see text], and show that the strain [Formula: see text] can not invade the system if [Formula: see text]. Further, the disease-free equilibrium of the system is globally asymptotically stable if [Formula: see text]. When [Formula: see text], strain [Formula: see text] dominance equilibrium [Formula: see text] exists, and is locally asymptotically stable when [Formula: see text], [Formula: see text], [Formula: see text]. Then, by applying Lyapunov–LaSalle techniques, we establish the global asymptotical stability of the dominance equilibrium corresponding to some strain [Formula: see text]. This implies strain [Formula: see text] eliminates the other strain as long as [Formula: see text], where [Formula: see text] denotes the probability of a given susceptible mosquito being transmitted by a primarily infected human with strain [Formula: see text]. Finally, we study the existence of the coexistence equilibria under some conditions.

Contributions and problems of mathematical models in COVID‐19 prevention in Japan

AbstractThis article reviews the essential role of mathematical models in understanding and combatting the pandemic of novel coronaviruses, in particular focusing the advance in the use of mathematical models in disease control in Japan. Highlighting the integral role of mathematical models in public health, the article introduces a model that factors in the heterogeneity of infectious contacts, concentrating on the effectiveness of testing and isolation, alongside a model that involves economic losses. The models exhibit how, given such heterogeneity, milder behavioral restrictions can still achieve suppression, rigorous testing and isolation can effectively curb the spread, and containment measures can mitigate economic losses. These models aid in grasping the complicated dynamics of disease transmission and optimizing interventions. The knowledge of population ecology is also considered effective for public health in statistical analysis, organizing concepts using dynamic mathematical models, which lead to policy proposals and deepen understanding. Evolution theory may help the understanding of virulence subject to change. However, effective prevention necessitates not only models but also the practical implementation of efficacious measures. The cooperation of various disciplines is particularly crucial in achieving a balance between health measures, economic interests, and human rights. Moreover, the article acknowledges the limitations of models and underscores the significance of real‐world execution. Overall, the article advocates for a broader outlook to tackle future pandemics and related challenges, underscoring the importance of ongoing academic cooperation and global governance to effectively address emerging infectious diseases and their far‐reaching implications.

Effectiveness of selected small group teaching methods for undergraduate medical students on basic concepts of epidemiology: A quasi-experimental study

BACKGROUND: Teaching epidemiology to young medical students using traditional teaching techniques is fraught with myriad challenges. Incorporating innovative small group teaching (SGT) approaches that promote active learning, practical application, and critical thinking can help in overcoming these challenges. AIM/OBJECTIVE: To identify the most effective SGT method from selected three approaches [tutorial technique (TT), problem-based learning (PBL), and fishbowl technique (FBT)] to teach the basic concepts of epidemiology to the third-year undergraduate medical students of a private medical college in Puducherry, Southern India. MATERIALS AND METHODS: A quasi-experimental study was conducted among third-year undergraduate medical students for 6 months. The sample size was calculated to be 60 using the nMaster 2.0 sample size software. Three groups were formed with 20 students each. A pre-test, which included fifty multiple-choice questions covering topic one, was conducted for students in all three groups. An SGT session on topic one (dynamics of disease transmission) was held on the same day by different facilitators for three groups A, B, and C using the TT, PBL, and FBT, respectively. After 6 weeks of the SGT session for topic one, a post-test using the same questions was organized for all three groups to identify the effectiveness of each SGT method. The above sequence of events was followed for topic two (study designs) and topic three (investigation of disease outbreak) among all groups in the subsequent months. A written informed consent was sought from all students. The collected data was entered in MS Excel 2010 and analyzed using SPSS 21. The pre- and post-tests for all topics in all three groups were compared using a paired t-test, and an ANOVA test was used to find any difference between the groups. RESULTS: The mean post-test score in each of the three groups for all topics had improved when compared with the mean pre-test score, which was significantly different between the three groups. Further, the mean score of group B (PBL group) was found to be higher than group C (FBT) but not significantly higher compared to group A (TT). The mean score of the feedback where the participants were asked to rate the overall session was found to be high in group B (PBL) followed by group A (TT). CONCLUSION: PBL and TT were found to be an equally effective way of small group methods for teaching–learning epidemiology in medical school.

A Caputo fractional derivative dynamic model of hepatitis E with optimal control based on particle swarm optimization

Hepatitis E, as a zoonotic disease, has been a great challenge to global public health. Therefore, it has important research value and practical significance for the transmission and control of hepatitis E virus (HEV). In the exploration of infectious disease transmission dynamics and optimal control, mathematical models are often applied. Among them, the fractional differential model has become an important and practical tool because of its good memory and genetic characteristics. In this paper, an HEV propagation dynamic model is constructed by the Caputo fractional derivative. First, the properties of the model are analyzed, including the existence, non-negativity, boundedness, and stability of the equilibrium points. Then, from the perspective of fractional optimal control (FOC), control measures were proposed, including improving the awareness and prevention of hepatitis E among susceptible people, strengthening the treatment of infected people, and improving environmental hygiene. Then, an FOC model of HEV was constructed. After analyzing the necessary conditions for optimality, the particle swarm optimization is introduced to optimize the control function. In addition, four control strategies are applied. Finally, the numerical simulation is completed by the fractional Adams–Bashforth–Moulton prediction correction algorithm. The four strategies and no control were compared and analyzed. The numerical simulation results of different fractional orders are also compared and analyzed. The results illustrate that the optimal strategy, compared with no control, reduces the HEV control time by nearly 60 days. Therefore, this method would contribute to the study of HEV transmission dynamics and control mechanisms, thus contributing to the development of global public health.

A Study of a Class Continuous SIR Epidemic Model with History

The SIR model, an epidemiological model, divides a population into three compartments: Susceptible (S), Infected (I), and Recovered (R). It is widely used to understand the spread of infectious diseases and predict epidemic outcomes based on factors such as transmission rates and population dynamics. A deterministic epidemic mathematical model to describe the transmission dynamics of an infectious disease was constructed and analyzed by incorporating memory term which provides information on the current and past disease states. The model revealed two key equilibria: a disease-free equilibrium and an endemic equilibrium. The calculated basic reproductive number R0, was employed to establish that when R0&lt;1, the disease-free equilibrium is locally asymptotically stable, while the endemic equilibrium is locally asymptotically stable when R0&gt;1. Additionally, we explored the global stability of these equilibria using Lyapunov functions and Dulac's method, respectively. To validate our analytical findings, we conducted numerical simulations of the model which show the importance of history in the dynamic spread and elimination of disease.

Impact of Climate Change on Vector-Borne Diseases in Wildlife

Purpose: This study sought to investigate the impact of climate change on vector-borne diseases in wildlife.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings reveal that there exists a contextual and methodological gap relating to climate change on vector-borne diseases in wildlife. Preliminary empirical review revealed that that environmental shifts, driven by climate change, profoundly influenced the distribution and transmission dynamics of diseases among wildlife populations. Through a comprehensive review of empirical studies, it was found that warmer temperatures, altered precipitation patterns, and extreme weather events expanded vector habitats, increasing disease exposure for wildlife. The research emphasized the importance of interdisciplinary approaches and proactive measures, such as habitat restoration and disease surveillance, to mitigate disease risks and promote ecosystem resilience. Overall, the study highlighted the urgent need to address the interconnected challenges of climate change, vector-borne diseases, and wildlife health to safeguard environmental and human well-being.&#x0D; Unique Contribution to Theory, Practice and Policy: The Ecological Niche theory, Disease Ecology theory and One Health theory may be used to anchor future studies on climate change on vector-borne diseases in wildlife. The study provided recommendations that contributed to theory, practice, and policy. From a theoretical perspective, it emphasized the need for interdisciplinary research to understand the complex interactions between climate change, vector populations, and disease transmission. Practically, the study recommended adaptive management strategies, such as habitat restoration and disease surveillance, to mitigate disease risks in wildlife. Policy-wise, it called for integrating climate resilience and biodiversity conservation efforts, along with public education campaigns and international cooperation, to address the transboundary nature of vector-borne disease threats. Overall, these recommendations aimed to build resilience in ecosystems, protect biodiversity, and ensure the health of wildlife populations amidst climate change.