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Climate change is acknowledged to directly affect not only the environment, economy, and society but also the transmission dynamics of infectious diseases, thereby impacting public health. The recent experiences with the spread of SARS-CoV-2 and Monkeypox have highlighted the complex and interconnected nature of infectious diseases, which are strongly linked to various determinants of health. Considering these challenges, adopting a new vision such as the trans-disciplinary approach appears to be imperative. This paper proposes a new theory about viruses' spread, based on a biological model, accounting for the optimisation of energy and material resources for organisms' survival and reproduction in the environment. The approach applies Kleiber's law scaling theory, originally developed in biology, to model community dynamics in cities. A simple equation can be used to model pathogen spread without accounting for each species' physiology by leveraging the superlinear scaling of variables with population size. This general theory offers several advantages, including the ability to explain the rapid and surprising spread of both SARS-CoV-2 and Monkeypox. The proposed model shows similarities in the spreading processes of both viruses, based on the resulting scaling factors, and opens new avenues for research. By fostering cooperation and integrating knowledge from different disciplines to effectively tackle the multifaceted dimensions of disease outbreaks, we can work towards preventing future health emergencies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible., (Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.)

Assessing the risk of disease spread between communities is important in our highly connected modern world. However, the impact of disease- and population-specific factors on the time taken for an epidemic to spread between communities, as well as the impact of stochastic disease dynamics on this spreading time, are not well understood. In this study, we model the spread of an acute infection between two communities ('patches') using a susceptible-infectious-removed (SIR) metapopulation model. We develop approximations to efficiently evaluate the probability of a major outbreak in a second patch given disease introduction in a source patch, and the distribution of the time taken for this to occur. We use these approximations to assess how interventions, which either control disease spread within a patch or decrease the travel rate between patches, change the spreading probability and median spreading time. We find that decreasing the basic reproduction number in the source patch is the most effective way of both decreasing the spreading probability, and delaying epidemic spread to the second patch should this occur. Moreover, we show that the qualitative effects of interventions are the same regardless of the approximations used to evaluate the spreading time distribution, but for some regions in parameter space, quantitative findings depend upon the approximations used. Importantly, if we neglect the possibility that an intervention prevents a large outbreak in the source patch altogether, then intervention effectiveness is not estimated accurately., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances., Methods: Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible-infected-recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve., Results: The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010., Conclusions: The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

Art's many contributions to the quantitative modeling of spatial processes provide a solid foundation for generations of researchers to come. In this review, we take a roughly chronological path through Art's published research, starting with his work on spatial point processes, which demonstrated the frequent pattern of inhibition at a very local scale and clustering at a larger scale. In turn, this led to his development of local statistics for detecting hotspots and related inferential procedures. When data are spatially aggregated, the form of dependence is often specified using a weighting matrix and Art developed a flexible framework for specifying the matrix structure. His later research on the spread of infectious diseases was also important in understanding the nature of disease spread. Art's keen insights and his ability to formulate novel issues are apparent throughout his published works. [ABSTRACT FROM AUTHOR]

The SIR model is a differential equations based model of the spread of an infectious disease that compartmentalises individuals in a population into one of three states: those who are susceptible to a disease (S), those who are infected and can transmit the disease to others (I), and those who have recovered from the disease and are now immune (R). This Classroom Note describes how to initiate teaching the SIR model with two concrete physical simulations to provide students with first-hand experience with some of the nuanced behaviour of how an infectious disease spreads through a closed population. One simulation physically models disease spread by the exchange of fluids, using pH to simulate infection. A second simulation incorporates randomness through the use of a probability game to keep track of the state of each individual at each time step. Both simulations invite students to ask questions about what factors influence disease spread. The concrete experience from the physical simulations enables students to make connections to the abstract mathematical representation of the SIR model and discuss the sources of stochasticity present in the spread of an infectious disease. [ABSTRACT FROM AUTHOR]

The overall purpose of this paper is to define a new metric on the spreadability of a disease. Herein, we define a variant of the well-known graph-theoretic burning number (BN) metric that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the BN as the CN concentrates on first time infections. This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a novel methodology to solve for the CN of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis). We then employ Monte Carlo simulation to determine the average contagion number (ACN) (i.e., a mostlikely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are specifically designed to model human social networks (sociograms). We test our method on some randomly generated scale-free graphs and our findings indicate the CN to be a robust, tractable (the BN is NP-hard even for a tree), and effective disease spread metric for decision makers. The contributions herein advance disease spread understanding and reveal the importance of the underlying network structure. Understanding disease spreadability informs public policy and the associated managerial allocation decisions. [ABSTRACT FROM AUTHOR]

Several epidemics, such as the Black Death and the Spanish flu, have threatened human life throughout history; however, it is unclear if humans will remain safe from the sudden and fast spread of epidemic diseases. Moreover, the transmission characteristics of epidemics remain undiscovered. In this study, we present the results of an epidemic simulation experiment revealing the relationship between epidemic parameters and pandemic risk. To analyze the time-dependent risk and impact of epidemics, we considered two parameters for infectious diseases: the recovery time from infection and the transmission rate of the disease. Based on the epidemic simulation, we identified two important aspects of human safety with regard to the threat of a pandemic. First, humans should be safe if the fatality rate is below 100%. Second, even when the fatality rate is 100%, humans would be safe if the average degree of human social networks is below a threshold value. Nevertheless, certain diseases can potentially infect all nodes in the human social networks, and these diseases cause a pandemic when the average degree is larger than the threshold value. These results indicated that certain infectious diseases lead to human extinction and can be prevented by minimizing human contact. [ABSTRACT FROM AUTHOR]

Foot‐and‐mouth disease (FMD) is a highly contagious viral disease that affects ruminants and pigs. Countries with large exports of livestock products are highly vulnerable to economic damage following an FMD incursion. The faster disease spread is controlled, the lower the economic damage. During the past decades, the structure of livestock production has dramatically changed. To maintain the relevance of contingency plans, it is important to understand the effects of changes in herd structure on the spread and control of infectious diseases. In this study, we compare the spread and control of FMD based on 2006/2007 and 2018 livestock data. Spread of FMD in Denmark was simulated using the DTU‐DADS model, applying different control measures. The number of cattle, swine and sheep/goat herds reduced from about 50,000 in total in 2006/2007 to about 33,000 in 2018. During this period, the average number of outgoing animal movements and the exports of swine and swine products increased by about 35% and 22%, respectively. This coincided with an overall increase in herd size of 14%. Using the EU and national control measures (Basic: 3 days standstill, depopulation of detected herds followed by cleaning and disinfection and establishment of control zones, where tracing, surveillance and contact restrictions are implemented), we found that the simulated epidemics in 2018 would be about 50% shorter in duration, affect about 50% fewer herds but cause more economic damage, compared to epidemics using 2006/2007 data. When 2006/2007 data were used, Basic + pre‐emptive depopulation (Depop) overall was the optimal control strategy. When 2018 data were used, this was the case only when epidemics were initiated in cattle herds, whereas when epidemics were initiated in sow or sheep/goats herds, basic performed as well as Depop. The results demonstrate that regular assessment of measures to control the spread of infectious diseases is necessary for contingency planning. [ABSTRACT FROM AUTHOR]

Infectious diseases are a primary driver of bee decline worldwide, but limited understanding of how pathogens are transmitted hampers effective management. Flowers have been implicated as hubs of bee disease transmission, but we know little about how interspecific floral variation affects transmission dynamics. Using bumblebees (Bombus impatiens), a trypanosomatid pathogen (Crithidia bombi) and three plant species varying in floral morphology, we assessed how host infection and plant species affect pathogen deposition on flowers, and plant species and flower parts impact pathogen survival and acquisition at flowers. We found that host infection with Crithidia increased defaecation rates on flowers, and that bees deposited faeces onto bracts of Lobelia siphilitica and Lythrum salicaria more frequently than onto Monarda didyma bracts. Among flower parts, bracts were associated with the lowest pathogen survival but highest resulting infection intensity in bee hosts. Additionally, we found that Crithidia survival across flower parts was reduced with sun exposure. These results suggest that efficiency of pathogen transmission depends on where deposition occurs and the timing and place of acquisition, which varies among plant species and environmental conditions. This information could be used for development of wildflower mixes that maximize forage while minimizing disease spread. [ABSTRACT FROM AUTHOR]

Diffusion processes are governed by external triggers and internal dynamics in complex systems. Timely and cost-effective control of infectious disease spread critically relies on uncovering underlying diffusion mechanisms, which is challenging due to invisible infection pathways and time-evolving intensity of infection cases. Here, we propose a new diffusion framework for stochastic processes, which models disease spread across metapopulations by incorporating human mobility as topological pathways in a heterogeneous social system. We apply Bayesian inference with the stochastic Expectation-Maximization algorithm to quantify underlying diffusion dynamics in terms of exogeneity and endogeneity and estimate cross-regional infection flow based on Granger causality. The effectiveness of our proposed model is shown by using comprehensive simulation procedures (robustness tests with noisy data considering missing or delayed human case reporting in real situations) and by applying the model to real data from 15-y dengue outbreaks in Australia. [ABSTRACT FROM AUTHOR]

Background: Infectious keratoconjunctivitis (IKC) is an ocular infectious disease caused by Mycoplasma conjunctivae which affects small domestic and wild mountain ruminants. Domestic sheep maintain the pathogen but the detection of healthy carriers in wildlife has raised the question as to whether M. conjunctivae may also persist in the wild. Furthermore, the factors shaping the dynamics of IKC outbreaks in wildlife have remained largely unknown. The aims of this study were (1) to verify the etiological role of M. conjunctivae in IKC outbreaks recorded between 2002 and 2010 at four study sites in different regions of France (Pyrenees and Alps, samples from 159 Alpine ibex Capra ibex, Alpine chamois Rupicapra rupicapra and Pyrenean chamois Rupicapra pyrenaica); (2) to establish whether there existed any epidemiological links between the different regions through a cluster analysis of the detected strains (from 80 out of the 159 animals tested); (3) to explore selected pathogen, host and environmental factors potentially influencing the dynamics of IKC in wildlife, by joining results obtained by molecular analyses and by field observations (16,609 animal observations). All of the samples were tested for M. conjunctivae by qPCR, and cluster analysis was based on a highly variable part of the lppS gene. Results: We documented infections with M. conjunctivae in epidemic and endemic situations, both in symptomatic and asymptomatic animals. The identified M. conjunctivae strains were site-specific and persisted in the local wild population for at least 6 years. In epidemic situations, peaks of cases and disease resurgence were associated with the emergence of new similar strains in a given area. Social interactions, seasonal movements and the landscape structure such as natural and anthropogenic barriers influenced the spatio-temporal spread of IKC. Adults were more affected than young animals and host susceptibility differed depending on the involved strain. Conclusion: Our study indicates that IKC is a multifactorial disease and that M. conjunctivae can persist in wildlife populations. The disease course in individual animals and populations is influenced by both host and mycoplasma characteristics, and the disease spread within and among populations is shaped by host behavior and landscape structure. [ABSTRACT FROM AUTHOR]

Emerging infectious diseases are a substantial threat to native populations. The spread of disease through naive native populations will depend on both demographic and disease parameters, as well as on habitat suitability and connectivity. Using the potential spread of squirrelpox virus ( SQPV) on the Isle of Arran as a case study, we develop mathematical models to examine the impact of an emerging disease on a population in a complex landscape of different habitat types. Furthermore, by considering a range of disease parameters, we infer more generally how complex landscapes interact with disease characteristics to determine the spread and persistence of disease. Specific findings indicate that a SQPV outbreak on Arran is likely to be short lived and localized to the point of introduction allowing recovery of red squirrels to pre-infection densities; this has important consequences for the conservation of red squirrels. More generally, we find that the extent of disease spread is dependent on the rare passage of infection through poor quality corridors connecting good quality habitats. Acute, highly transmissible infectious diseases are predicted to spread rapidly causing high mortality. Nonetheless, the disease typically fades out following local epidemics and is not supported in the long term. A chronic infectious disease is predicted to spread more slowly but can remain endemic in the population. This allows the disease to spread more extensively in the long term as it increases the chance of spread between poorly connected populations. Our results highlight how a detailed understanding of landscape connectivity is crucial when considering conservation strategies to protect native species from disease threats. [ABSTRACT FROM AUTHOR]

Diseases spreading over humans and animals is a ubiquitous process that has attracted much attention recently. To deeply explore the dynamics of the disease spread process on physical-contact networks, considering immunity exists in the population and dynamical behavior of viral transmission that have similar characteristics to cascading failure model based on complex network, the cascading failure model with load distribution parameters and epidemiological SIR model is proposed based on the local characteristic of nodes. Using this model, we investigate the virus outbreak conditions of Erdős–Rény (ER) and Albert–László Barabási (BA) networks, the formulas of critical values for networks recovery are derived. With numerical simulations, we obtain the relationship between the resilience of the networks against risk and the node redundancy factor. Furthermore, the intrinsic link between virus propagation rate and redundancy coefficient is obtained with the dynamical analysis, which is crucial for keeping networks robust against perturbations. These findings may help expand the understanding of the dynamic interactions between viral transmission and cascading failure, encouraging further study on the control and prevention of infectious disease spread. [ABSTRACT FROM AUTHOR]

To effectively prepare for a pandemic disease outbreak, knowledge of how the disease will spread is paramount. The global outbreak of severe acute respiratory syndrome (SARS) in 2002-2003 highlighted the need for such data. This need is also apparent in preparing for and responding to all disease outbreaks, from pandemic influenza to avian flu. Many previous studies of disease make simplistic assumptions about transmission and infection rates and assume that each member of the population is identical or homogeneous. We propose an agent-based simulation model that treats each individual as unique, with nonhomogeneous transmission and infection rates correlated to demographic information and behavior. The results of the model are output to geographic information system software to provide a map of the estimated disease spread area, which can be used as a policy-making tool for determining a suitable mitigation strategy. The Ontario Agency for Health Protection and Promotion (OAHPP) uses the model for pandemic planning for the Greater Toronto area in Ontario, Canada. [ABSTRACT FROM AUTHOR]

Simple Summary: Genetic diversity in wildlife is a matter of growing concern in contexts related to disease transmission and human health. Tuberculosis is a zoonotic disease with relevant consequences and can present high prevalence in wild boar and red deer populations. Here, we review studies on the genetic diversity of ungulates, wild boar, and red deer, and assess to what extent these studies consider its importance in the spread of disease. The relationship between host genetic diversity and the probability of disease spread is illustrated in Spanish populations of wild boar and red deer. Host genetic diversity tends to limit disease spread in nature and buffers populations against epidemics. Genetic diversity in wildlife is expected to receive increasing attention in contexts related to disease transmission and human health. Ungulates such as wild boar (Sus scrofa) and red deer (Cervus elaphus) are important zoonotic hosts that can be precursors to disease emergence and spread in humans. Tuberculosis is a zoonotic disease with relevant consequences and can present high prevalence in wild boar and red deer populations. Here, we review studies on the genetic diversity of ungulates and determine to what extent these studies consider its importance on the spread of disease. This assessment also focused on wild boar, red deer, and tuberculosis. We found a disconnection between studies treating genetic diversity and those dealing with infectious diseases. Contrarily, genetic diversity studies in ungulates are mainly concerned with conservation. Despite the existing disconnection between studies on genetic diversity and studies on disease emergence and spread, the knowledge gathered in each discipline can be applied to the other. The bidirectional applications are illustrated in wild boar and red deer populations from Spain, where TB is an important threat for wildlife, livestock, and humans. [ABSTRACT FROM AUTHOR]

Understanding risk factors for the spread of infectious diseases over time and across the landscape is critical for managing disease risk. While habitat connectivity and characteristics of local and neighboring animal (i.e., host) assemblages are known to influence the spread of diseases, the interactions among these factors remain poorly understood. In this study, we conducted a county-level analysis to test the effects of forest connectivity, together with the suitability of local assemblage (measured by the similarity of local host assemblage with neighboring assemblages) and the infection intensity of neighboring counties on the spatial expansion of Lyme disease in the United States. Our results suggested that both the similarity of local host assemblage and the infection intensity of neighboring counties were positively correlated with the probability of disease spread. Moreover, we found that increasing forest connectivity could facilitate the positive effect of neighbor infection intensity. In contrast, the effect size of the host assemblage similarity decreased with increasing connectivity, suggesting that host assemblage similarity was less effective in well-connected habitats. Our results thus indicate that habitat connectivity can indirectly influence disease spread by mediating the effects of other risk factors. [ABSTRACT FROM AUTHOR]

Since the introduction of African swine fever (ASF) into Georgia in 2007, the disease has been spreading in an unprecedented way. Many countries that are still free from the disease fear the emergence of ASF in their territory either in domestic pigs or in wild boar. In the past, ASF was often described as being a highly contagious disease with mortality often up to 100%. However, the belief that the disease might enter a naïve population and rapidly affect the entire susceptible population needs to be critically reviewed. The current ASF epidemic in wild boar, but also the course of ASF within outbreaks in domestic pig holdings, suggest a constant, but relatively slow spread. Moreover, the results of several experimental and field studies support the impression that the spread of ASF is not always fast. ASF spread and its speed depend on various factors concerning the host, the virus, and also the environment. Many of these factors and their effects are not fully understood. For this review, we collated published information regarding the spreading speed of ASF and the factors that are deemed to influence the speed of ASF spread and tried to clarify some issues and open questions in this respect. [ABSTRACT FROM AUTHOR]