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Abstract We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variant's epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants.

Understanding and modeling individuals' behaviors during epidemics is crucial for effective epidemic control. However, existing research ignores the impact of users' irrationality on decision-making in the epidemic. Meanwhile, existing disease control methods often assume users' full compliance with measures like mandatory isolation, which does not align with the actual situation. To address these issues, this paper proposes a prospect theorybased framework to model users' decision-making process in epidemics and analyzes how irrationality affects individuals' behaviors and epidemic dynamics. According to the analysis results, irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high. Then, this paper proposes a behavior inducement algorithm to guide individuals' behaviors and control the spread of disease. Simulations and real user tests validate our analysis, and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals' behavior. [ABSTRACT FROM AUTHOR]

Opioid misuse continues to cause significant harm. To investigate current research, we conducted a scoping literature review of disease spread models of opioid misuse from January 2000 to December 2022. In total, 85 studies were identified and examined for the opioids modeled, model type, data sources used and model calibration and validation. Most of the studies (58%, 49) only modeled heroin; the next largest categories were prescription opioids and unspecified opioids which accounted for 9% (8) each. Most models were theoretical compartmental models (57) or applied compartmental models (21). Previously published research was the most used data source (38), and a majority of the model validation involved the researchers setting initial conditions to verify theoretical results (30). To represent typical opioid use more accurately, multiple opioids need to be incorporated into the disease spread models, and applying different modeling techniques may allow other insights into opioid misuse spread. [ABSTRACT FROM AUTHOR]

Abstract African swine fever (ASF) is an infectious and highly fatal disease affecting wild and domestic swine, which is unstoppably spreading worldwide. In Europe, wild boars are one of the main drivers of spread, transmission, and maintenance of the disease. Landscape connectivity studies are the main discipline to analyze wild-species dispersal networks, and it can be an essential tool to predict dispersal-wild boar movement routes and probabilities and therefore the associated potential ASF spread through the suitable habitat. We aimed to integrate wild boar habitat connectivity predictions with their occurrence, population abundance, and ASF notifications to calculate the impact (i.e., the capacity of a landscape feature to favor ASF spread) and the risk (i.e., the likelihood of a habitat patch becoming infected) of wild boar infection across Europe. Furthermore, we tested the accuracy of the risk of infection by comparing the results with the temporal distribution of ASF cases. Our findings identified the areas with the highest impact and risk factors within Europe's central and Eastern regions where ASF is currently distributed. Additionally, the impact factor was 31 times higher on habitat patches that were infected vs non-infected, proving the utility of the proposed approach and the key role of wild boar movements in ASF-spread. All data and resulting maps are openly accessible and usable.

Within-host interactions among coinfecting parasites can have major consequences for individual infection risk and disease severity. However, the impact of these within-host interactions on between-host parasite transmission, and the spatial scales over which they occur, remain unknown. We developed and apply a novel spatially explicit analysis to parasite infection data from a wild wood mouse (Apodemus sylvaticus) population. We previously demonstrated a strong within-host negative interaction between two wood mouse gastrointestinal parasites, the nematode Heligmosomoides polygyrus and the coccidian Eimeria hungaryensis, using drug-treatment experiments. Here, we show this negative within-host interaction can significantly alter the between-host transmission dynamics of E. hungaryensis, but only within spatially restricted neighbourhoods around each host. However, for the closely related species E. apionodes, which experiments show does not interact strongly with H. polygyrus, we did not find any effect on transmission over any spatial scale. Our results demonstrate that the effects of within-host coinfection interactions can ripple out beyond each host to alter the transmission dynamics of the parasites, but only over local scales that likely reflect the spatial dimension of transmission. Hence there may be knock-on consequences of drug treatments impacting the transmission of non-target parasites, altering infection risks even for non-treated individuals in the wider neighbourhood. [ABSTRACT FROM AUTHOR]

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]

We study the following model of disease spread in a social network. At first, all individuals are either infected or healthy. Next, in discrete rounds, the disease spreads in the network from infected to healthy individuals such that a healthy individual gets infected if and only if a sufficient number of its direct neighbors are already infected. We represent the social network as a graph. Inspired by the real-world restrictions in the recent epidemic, especially by social and physical distancing requirements, we restrict ourselves to networks that can be represented as geometric intersection graphs. We show that finding a minimal vertex set of initially infected individuals to spread the disease in the whole network is computationally hard, already on unit disk graphs. Hence, to provide some algorithmic results, we focus ourselves on simpler geometric graph classes, such as interval graphs and grid graphs. [ABSTRACT FROM AUTHOR]

This research presents a classification methodology for the detection of new or emerging stress in trees using indices derived from hyperspectral data and tests whether existing hyperspectral indices are effective when used as the classification features for this problem. We tested six existing indices—Water Band Index (WBI), Gitelson–Merzlyak B Index (GMb), Normalized Phaeophytization Index (NPQI), Combined Carotenoid/Chlorophyll Ratio Index (CCRI), Photochemical Reflectance Index (PRI), and Red-Edge Chlorophyll Index (CIre)—along with a seventh Test Index—generated as a composite of PRI and Cire—as classification features. Analysis was conducted using data collected from trees with and without emerald ash borer (EAB) infestation to develop a methodology that could be adapted to measure emerging stress from other pathogens or invasive pests in other tree species. Previous work has focused specifically on the identification of damage or stress symptoms caused by a specific pathogen. In this study, we adapted that work to develop a system of classification that can be applied to the identification of stress symptoms from a range of sources, measurable in trees based on spectral response and, in some cases, detectable prior to the onset of visible symptoms that can be measured through human observation. Our data indicate that existing indices derived from hyperspectral data are effective as classification features when measuring spectral responses indicative of emerging stress in trees. [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]

Simple Summary: Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory disease. MERS-CoV is a zoonotic virus that spreads between animals and humans. The objectives of this research include simulation of MERS-CoV spread using a customized animal disease spread model (i.e., customized stochastic model for the United Arab Emirates (UAE); analyzing the MERS-CoV spread and prevalence based on camel age groups and identifying the control MERS-CoV strategies to aid the decision-maker in the selection of the optimum strategy to control the spread of the disease. The results of this research conclude that movement control is the optimum "best" strategy to control the spread of MERS-CoV. Middle East Respiratory Syndrome (MERS-CoV) is a coronavirus-caused viral respiratory infection initially detected in Saudi Arabia in 2012. In UAE, high seroprevalence (97.1) of MERS-CoV in camels was reported in several Emirate of Abu Dhabi studies, including camels in zoos, public escorts, and slaughterhouses. The objectives of this research include simulation of MERS-CoV spread using a customized animal disease spread model (i.e., customized stochastic model for the UAE; analyzing the MERS-CoV spread and prevalence based on camels age groups and identifying the optimum control MERS-CoV strategy. This study found that controlling animal mobility is the best management technique for minimizing epidemic length and the number of affected farms. This study also found that disease dissemination differs amongst camels of three ages: camel kids under the age of one, young camels aged one to four, and adult camels aged four and up; because of their immunological state, kids, as well as adults, had greater infection rates. To save immunization costs, it is advised that certain age groups be targeted and that intense ad hoc unexpected vaccinations be avoided. According to the study, choosing the best technique must consider both efficacy and cost. [ABSTRACT FROM AUTHOR]

Research on pandemics in institutional settings often assumes that all human interactions within a jail pose similar viral transmission risks. We developed an agent-based model (ABM) called Simulation Applications for Forecasting Effective Responses in Corrections (SAFER-C™) to simulate nine scenarios of possible interactions and virus transmission among incarcerated individuals and jail staff and tested this assumption. We found that resumption of high-contact activities has a greater impact on the number of infections, while out-of-cell group sizes and initial vaccination rates had lower impact. This work emphasizes the importance of understanding and modeling human interactions in confinement facilities, as well as understanding, responding to, and limiting the mechanism of viral transmission in jails. Insights from ABMs provide correctional administrators with realistic options for managing responses. [ABSTRACT FROM AUTHOR]

Tomato golden mosaic disease, caused by the tomato severe rugose virus (ToSRV), is one of the most serious tomato diseases. ToSRV is transmitted by the whitefly Bemisia tabaci MEAM1, from a wide range of hosts. Introducing ToSRV in commercial fields probably can occur by transporting the virus from distant locations. However, our hypothesis is that the inoculum causing epidemic development might be related to nearby sources. This study evaluated the short‐range flight distance of B. tabaci MEAM1 (marked with detectable proteins and powder dye), through recaptures in yellow sticky traps, and the transmission of ToSRV by viruliferous B. tabaci MEAM1 to tomato plants placed at increasing distances from a release point. Evaluated whiteflies reached a maximum of 30 m from the release point, and no ToSRV‐infected tomato plants were detected at greater distances. These findings reinforce the suspicion that the epidemic‐supporting source of ToSRV inoculum must be close to commercial cultivation areas. About the efficacy of the type of markers used, of the two protein sources tested (pasteurized chicken egg‐white albumin and commercial soy‐based extracts as sources of albumin and lecithin, respectively), albumin was considered easier to detect. Marking with these proteins or with two dyes (blue or pink) did not affect B. tabaci MEAM1 mortality, although field tests showed that fewer marked than unmarked insects were recaptured. Dye marking was considered more practical because of the ease of detection. The study validates a new tool to understand the movement of B. tabaci MEAM1, based on different marking methods. [ABSTRACT FROM AUTHOR]

Abstract Background Climate affects the thermal adaptation and distribution of hosts, and drives the spread of Chytridiomycosis—a keratin-associated infectious disease of amphibians caused by the sister pathogens Batrachochytrium dendrobatidi (Bd) and B. salamandrivorans (Bsal). We focus on their climate-pathogen relationships in Eurasia, the only region where their geographical distributions overlap. Eurasia harbours invaded and native areas of both pathogens and the natural habitats where they co-exist, making it an ideal region to examine their environmental niche correlations. Our understanding of how climate change will affect their distribution is broadened by the differences in climate correlates and niche characteristics between Bd and Bsal in Asia and Europe. This knowledge has potential conservation implications, informing future spread of the disease in different regions. Results We quantified the environmental niche overlap between Bd and Bsal in Eurasia using niche analyses. Results revealed partial overlap in the niche with a unique 4% of non-overlapping values for Bsal, suggesting segregation along certain climate axes. Bd tolerates higher temperature fluctuations, while Bsal requires more stable, lower temperature and wetter conditions. Projections of their Realized Climatic Niches (RCNs) to future conditions show a larger expansion of suitable ranges (SRs) for Bd compared to Bsal in both Asia and Europe, with their centroids shifting in different directions. Notably, both pathogens' highly suitable areas in Asia are expected to shrink significantly, especially under the extreme climate scenarios. In Europe, they are expected to expand significantly. Conclusions Climate change will impact or increase disease risk to amphibian hosts, particularly in Europe. Given the shared niche space of the two pathogens across available climate gradients, as has already been witnessed in Eurasia with an increased range expansion and niche overlap due to climate change, we expect that regions where Bsal is currently absent but salamanders are present, and where Bd is already prevalent, may be conducive for the spread of Bsal.

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]

Understanding how individuals come into contact with each other is important in many fields from biology and ecology to robotics and physics. Interaction dynamics are central in understanding how information is spread between agents, how disease spreads through a population, and how group movement or behaviour occurs. However, in many applications, the underlying mode of movement is not considered, and instead, contacts are considered a fraction of all possible contacts amongst a population. This gives rise to the mass-action law which in turn implies a negative quadratic relationship between contacts and individuals. Here we consider how a simple but often used movement model, the correlated random walk, affects the contact rate in a standard Susceptible-Infection (SI) epidemiological model. Via extensive simulation, we show that the contact rate is not always well described by the assumed negative quadratic relationship, I (N - I) (where I is the number of infected at a given time and N the total number of individuals). Instead, we find that a contact rate proportional to I (N - I) α with 0 < α ≤ 1 is a better qualitative fit, where α depends upon parameters such as the straightness of the movement and the density of individuals. We highlight that the expected contacts at low densities increase with straight line movement, whereas, at high densities, they increase with more random movement. [ABSTRACT FROM AUTHOR]

This study determined the diversity of Cicadellidae species infesting various field crops (i.e., wheat, barley, chickpeas, lentils, corn, vegetables, sunflower, peanuts, onion, sesame, tobacco, watermelon, melon, tomato, pepper, eggplant, alfalfa, cucumber, bean, potato) grown in southeastern Anatolia region, Turkey. The second major aim of the study was to determine whether the species could serve as potential vectors for viruses and phytoplasma diseases. Field surveys were conducted through 2015 and 2016 in Adıyaman, Diyarbakır, Mardin and Şanlıurfa provinces situated in southeastern Anatolia region of the country. The survey timing was adjusted according to the phenological stages of the crops grown in the region. Samples were collected either by D-Vac or sweep net based on the phenological stage of the surveyed crop. A total of 100 net sweeps were swept at 5 different places in the field (20 nets at each sampling place) or D-Vac was run for 2 minutes at five different random places within the field. A total of 33 species belonging to 7 Cicadellidae sub-families were recorded. The most frequently observed species in the region were Asymmetrasca decedens, Empoasca decipiens, Zyginidia sohrab, Z. pullula, Circulifer haematoceps, Psammotettix striatus, P. alienus and P. provincialis. A total of 26, 16, 12 and 11 species were recorded from Diyarbakır, Şanlıurfa, Mardin and Adıyaman provinces, respectively. The study suggests that P. alienus, P. provincialis, P. striatus and C. haematoceps were frequently recorded from the surveyed region and could be considered as potential vectors of viruses and pyhtoplasma in maize, wheat and other crops. Therefore, further studies should be focused on disease spread through reported species in the current study. [ABSTRACT FROM AUTHOR]

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model. [ABSTRACT FROM AUTHOR]

Aim: To determine the effects of obesity in childhood on SARS‐CoV‐2 infection. Methods: A population‐based, cross‐sectional study combining the Israeli Growth Survey and COVID‐19 data for children with at least one SARS‐CoV‐2 test from 16 February 2020 to 20 December 2021. Overweight and obesity status were based on body mass index and the Center for Disease Control criteria. Multivariate logistics regression was performed to validate reliability for weight categories at the age of approximately 6 years compared with weights at approximately 12 years. Results: A total of 444 868 records for children with an overall positivity rate of 22% were studied. The mean age was 9.5 years. The odds ratios of children with obesity or overweight after controlling for sex at 6 years to test positive were 1.07–1.12 and 1.06–1.08 (depending on the model), respectively, compared to those with healthy range body mass index. Conclusion: Excess weight appears to increase the risk of SARS‐CoV‐2 infection. This finding should be considered for public health planning. For example, children with overweight and obesity should be prioritised for vaccination. Excess weight in childhood can be harmful at a young age and not only for long‐term health. [ABSTRACT FROM AUTHOR]

The new Acute Respiratory Syndrome, COVID-19, has affected the health and the economy worldwide. Therefore, scientists have been looking for ways to understand this disease. In this context, the main objective of this study was the spatialization of COVID-19, thinking in distinguishing areas with high transmissibility yet, verifying if these areas were associated with the elderly population occurrence. The work was delineated, supposing that spatialization could support the decision-making to combat the outbreak and that the same method could be used for spatialization and prevent other diseases. The study area was a municipality near Sao Paulo Metropolis, one of Brazil's main disease epicenters. Using official data and an empirical Bayesian model, we spatialized people infected by region, including older people, obtaining reasonable adjustment. The results showed a weak correlation between regions infected and older adults. Thus, we define a robust model that can support the definition of actions aiming to control the COVID-19 spread. [ABSTRACT FROM AUTHOR]

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Insect-borne viral diseases epidemics are influenced by interaction among host plants, viruses, their vectors, and environment. Two species of mealybugs i.e. Planococcus minor and Ferrisia virgata are known as virus vectors that cause mottle disease on black pepper (Piper nigrum). Research was conducted to determine the main factors affecting mottle disease spread in the field. Study was conducted by observing 30 productive black pepper plants at the experimental field in Sukabumi, West Java. The parameters involved abundance of mealybugs, severity of mottle disease, and yield of black pepper plants. Disease severity and plant yield was assessed for three consecutive harvest seasons, while mealybug abundance was observed every two months for a year. The results showed that abundance of mealybugs did not have significant affect to disease severity and plant yield, indicating the vectors in the field does not play as a prominent role in disease spreading. Simple linear regression between disease severity and plant yield showed coefficient determination of R2 value about 0.4351 with negative correlation, indicating opposite effect between severity and plant yield. Increasing disease severity affect moderately plant yield decrease. Therefore, planting virus free seed stocks and applying good cultivation practices in the field will inhibit disease development and spreading which in turn will affect continuous optimal plant yields.

Lacking a federal policy to control the spread of COVID-19, state governors ordered lockdowns and mask mandates, at different times, generating a massive natural experiment. The authors exploit this natural experiment to address four issues: (1) Were lockdowns effective in reducing infections? (2) What were the costs to consumers? (3) Did lockdowns increase (signaling effect) or reduce (substitution effect) consumers' mask adoption? (4) Did governors' decisions depend on medical science or nonmedical drivers? Analyses via difference-in-differences and generalized synthetic control methods indicate that lockdowns causally reduced infections. Although lockdowns reduced infections by 480 per million consumers per day (equivalent to a reduction of 56%), they reduced customer satisfaction by 2.2%, consumer spending by 7.5%, and gross domestic product by 5.4% and significantly increased unemployment by 2% per average state by the end of the observation period. A counterfactual analysis shows that a nationwide lockdown on March 15, 2020, would have reduced total cases by 60%, whereas the absence of any state lockdowns would have resulted in five times more cases by April 30. The average cost of reducing the number of cases by one new infection was about $28,000 in lower gross domestic product. [ABSTRACT FROM AUTHOR]

Empirical observations and mathematical models show that climate warming can lead to the northern (or, more generally, poleward) spread of host species ranges and their corresponding diseases. Here, we consider an unexpected possibility whereby climate warming facilitates disease spread in the opposite direction to the directional shift in the host species range. To explore this possibility, we consider two host species, both susceptible to a disease, but spatially isolated due to distinct thermal niches, and where prior to climate warming the disease is endemic in the northern species only. Previous theoretical results show that species distributions can lag behind species thermal niches when climate warming occurs. As such, we hypothesize that climate warming may increase the overlap between northern and southern host species ranges, due to the northern species lagging behind its thermal tolerance limit. To test our hypothesis, we simulate climate warming as a reaction-diffusion equation model with a Susceptible-Infected (SI) epidemiological structure, for two competing species with distinct temperature-dependent niches. We show that climate warming, by shifting both species niches northwards, can facilitate the southward spread of disease, due to increased range overlap between the two populations. As our model is general, our findings may apply to viral, bacterial, and prion diseases that do not have thermal tolerance limits and are inextricably linked to their hosts distributions, such as the spread of rabies from arctic to red foxes. [ABSTRACT FROM AUTHOR]

Multiple introductions of SARS-COV-2 Omicron variant BA.1 and BA.1.1. lineages to Finland were detected in early December 2021. Within 3 weeks, Omicron overtook Delta as the most common variant in the capital region. Sequence analysis demonstrated the emergence and spread through community transmission of a large cluster of BA.1.1 virus.

Background: Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective: This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods: We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results: Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions: Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies. [ABSTRACT FROM AUTHOR]

White‐nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006–2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the current and predicting the future status and trends of bat species. We developed a model of disease spread that simultaneously achieves high‐predictive performance, computational efficiency, and interpretability. We modeled white‐nose syndrome spread using Gaussian process variations to infer the spread rate of the disease front, identify areas of anomalous time of arrival, and provide future forecasts of the expected time of arrival throughout North America. Cross‐validation of model predictive performance identified a stationary Gaussian process without an additional residual error process as the best‐supported model. Results indicated that white‐nose syndrome is likely to spread throughout the entire continental United States by 2030. These annually updatable model predictions will be useful in determining the horizon over which disease management actions must take place as well as in status and trend assessments of disease‐affected bats. [ABSTRACT FROM AUTHOR]

A carlavirus, closely related to cowpea mild mottle virus (CPMMV) and spread by silverleaf whitefly (SLW) was reported affecting fresh market beans in a major Australian growing district in 2016. Further investigations of this virus were completed through regular surveys of crops, weeds and SLW in this district from 2016–2019. Sequencing of the 3'end of the virus genome from a range of samples detected four variants, referred to as CPMMV:A:FB5288 and CPMMV:A:S1 to S3. The distribution of these four variants in survey samples showed the dominant variant in French bean crops as CPMMV:A:FB5288. The surveys also showed disease impacts were limited to autumn and varied over time. This variation is attributed to the influence of rainfall on adult insect vector levels. The experimental host range of CPMMV:A:FB5288 was shown to be limited to the Phaseoleae plant tribe and included the Australian native species, Glycine canescens. French bean varieties showed a range of susceptibilities to this dominant sequence variant from highly tolerant to very susceptible. The tolerant varieties provide the local industry with some options for disease management where previously there were none. Genetic diversity studies further highlight the need for taxonomic reform of the species referred to as CPMMV. [ABSTRACT FROM AUTHOR]

Abstract African swine fever (ASF) was diagnosed for the first time in Romania in a backyard holding in Satu‐Mare County in July 2017. Since then, more than 3800 outbreaks occurred in the entire country. Disease control strategies in the backyard sector rely almost exclusively on reactive measures implemented upon appearance of clinical signs and laboratory confirmation of ASF. In our descriptive study, infection course and outbreak investigation data of 56 affected backyard holdings in Satu‐Mare County has been investigated. Early disease detection based on clinical signs appeared to be efficient. In the majority of outbreaks, ASF was detected within the first 2 weeks after the estimated virus introduction. A clinical phase of 2–8 days was observed before pigs either succumbed to the disease or control measures were implemented on affected farms. A moderate on‐farm transmissibility of ASF virus between pigs was observed. Four clusters of outbreaks were identified indicating virus perpetuation and transmission from farm to farm. To suspend infection chains, rapid intervention by isolating affected farms combined with effective biosecurity measures is required. However, due to the backyard peculiarities, quick and effective implementation of control measures has shown to be rather difficult.

The Russian Federation was officially recognized free from peste des petitts ruminants (PPR). As far as the disease infects both domestic and wild small ruminants, it is important to identify the level of the threat associated with the wild fauna diversity in the neighboring countries, where PPR outbreaks were reported. For that reason, habitats of various disease susceptible animal species were examined. Habitats of the wild susceptible animals were mapped for further examination of the interactions between different animal species using zoological research data; PPR outbreaks in wild animals were also designated in the map thus allowing for the detection of the potential routes of the infection spread in the population and introduction to the country. Analysis of the PPR epidemic situation in the country demonstrated that the disease cases were reported in wild mountain animals (ibices and moufflons) and migratory steppe animals (gazelles and saigas). Risk of this highly contagious viral disease spread in wild small ruminants in Mongolia was reported (probability 0.77). Expert survey was carried out for the determination of possible trends and factors of the infection introduction with the wild susceptible animals, through which small ruminant epizootologists assessed the risk probability. During the survey it was determined that PPR was expected to be introduced from Mongolia (probability 0.81), and of major significance were seasonal migrations of wild animal populations. The resulted semi-quantitative parameters of the potential risk can be recommended for the arrangement and implementation of measures aimed at prevention of PPR introduction and spread in the intact domestic and wild small ruminant populations inhabiting the territory of the Russian Federation.

Here we investigate the effects of extensive sociality and mobility on the oral microbiome of 138 Agta hunter–gatherers from the Philippines. Our comparisons of microbiome composition showed that the Agta are more similar to Central African BaYaka hunter–gatherers than to neighbouring farmers. We also defined the Agta social microbiome as a set of 137 oral bacteria (only 7% of 1980 amplicon sequence variants) significantly influenced by social contact (quantified through wireless sensors of short-range interactions). We show that large interaction networks including strong links between close kin, spouses and even unrelated friends can significantly predict bacterial transmission networks across Agta camps. Finally, we show that more central individuals to social networks are also bacterial supersharers. We conclude that hunter–gatherer social microbiomes are predominantly pathogenic and were shaped by evolutionary tradeoffs between extensive sociality and disease spread.

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between‐farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non‐stationary). Our results identified important areas in which in‐going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low‐risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high‐risk areas, and 6.20% were within medium‐risk areas. Only 5.37% of the animals entering low‐risk areas came from high‐risk areas. On the other hand, 4.91% of the animals in the high‐risk areas came from low‐ and medium‐risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk. [ABSTRACT FROM AUTHOR]

Context: Landscape structure influences the spread of plant pathogens, including coffee leaf rust, a fungal disease affecting the coffee industry. Rust transmission is likely affected by landscape structure through the dispersal of wind-borne spores. Previous studies found positive associations between rust incidence and the proportion of pasture cover, suggesting deforestation may facilitate spore dispersal. Objectives: We explored the links between landscape structure and coffee rust by modeling the spread of rust transmission. We investigated how (1) spatial clustering of coffee farms, (2) proportion of landscape deforestation, and (3) clustering of deforestation affects the speed of rust transmission. Methods: We developed a probabilistic model to simulate within-patch and between-patch transmission in simulated and real landscapes. We modeled within-patch transmission using a probabilistic cellular automata model and between-patch transmission using a random walk with spore movement inhibited by canopy cover. Results: Clustering of coffee farms is the primary driver of rust transmission. Deforestation is a secondary driver of rust spread: outbreaks spread more rapidly in landscapes where deforested areas are evenly dispersed throughout the landscape. When applied to real landscapes in Costa Rica, the model yields the same trends as simulated landscapes and suggests increased amounts of coffee near the starting location of the outbreak are correlated with more rapid rust spread. Conclusions: It is essential to consider landscape structure when managing the spread of crop diseases. Increasing the spacing between coffee farms and reducing forest fragmentation in coffee-growing regions can benefit biodiversity conservation and reduce the economic impacts of coffee rust. [ABSTRACT FROM AUTHOR]

The informal economy absorbs a large share (more than 80%) of the labour force in sub-Saharan Africa (SSA). We examine whether a high level of informality tends to exacerbate the spread of the COVID-19. Using publicly available data for a cross-section of 46 SSA, we find evidence that confirms this hypothesis. [ABSTRACT FROM AUTHOR]

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Background: Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments. Objective: The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments. Methods: STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments. Results: As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480 (98.18%, 95% CI 97.9%-98.4%) results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups (high: 71.7%, 95% CI 70.3%-73.1%; moderate: 68.3%, 95% CI 66.0%-70.5%; low: 63.1%, 59.5%-66.6%). Conclusions: Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance. [ABSTRACT FROM AUTHOR]

Hymenoscyphus fraxineus, causal agent of Ash Dieback, has posed a threat to Fraxinus excelsior (common ash) in Europe since the 1990s. In south-western Europe, optimal climatic conditions for H. fraxineus become scattered and host density decreases, reducing disease spread rates. To date, the Ash Dieback agent has not been reported from southern and most of central Italy, where native F. excelsior is present as small fragmented populations. This study examines the expansion of Ash Dieback into central Italy, and it considers the consequences of further local spread with regards to the loss of F. excelsior genetic resource. Symptomatic F. excelsior were sampled from sixteen sites in northern and central Italy during 2020. Specimens were analyzed with a culturomics and a quantitative PCR approach. A bibliographic search of F. excelsior floristic reports was conducted for the creation of a detailed range map. The combined use of both techniques confirmed the presence of H. fraxineus in all the sites of central Italy where host plants were symptomatic. These new records represent the southern limit of the current known distribution of this pathogen in Italy, and together with Montenegro, in Europe. The characterization of the F. excelsior scattered range suggests that further spread of Ash Dieback across southern Italy is a realistic scenario. This presents a threat not just to the southern European proveniences of F. excelsior, but to the species as a whole, should Ash Dieback lead to the loss of warm climate adapted genetic material, which may become increasingly valuable under climate change. [ABSTRACT FROM AUTHOR]

COVID-19 is currently considered one of the greatest threats to health, safety, and the economy worldwide. An intense movement of international passengers may be critical for either the contingency or the spread of disease in a pandemic situation. Various research on international and national epidemics and their evolution have already been addressed in the medical literature. However, there are few studies to measure the influence of air transport on the proliferation of COVID-19 in Brazil yet. Thus, this article aims to verify if the movement of international air passengers influenced the COVID-19 spreading in Brazil. Therefore, air traffic and its concentration were analyzed, trying to identify its main entrances and volume of passengers. Afer that, statistical regression methods were used to estimate how passengers coming from countries contaminated by COVID-19 influenced the spread of the disease in Brazil. As a main result, it was found that foreign passengers disembarked in Brazil are directly related to the cases of COVID-19 in the country, making air transport a gateway to the virus in Brazil.

Abstract Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

In spite of current foot-and-mouth disease (FMD) preventive measures, the disease outbreaks are annually reported in different countries of the world. FMD tends to extensive spread and growing into epidemics. While being a transboundary infection according to the OIE/FAO classification, FMD severely affects the economy and international trade. The paper describes the analysis of the data on global spread of Type Asia-1 virus-induced FMD in 1999–2019. The virus of this type is most often reported in such Asian countries as Afghanistan, Pakistan, China, Nepal, Iran, Myanmar, from where it can spread to FMD free countries. In China, Asia-1 FMD outbreaks were reported from 2001 to 2009. Previously exotic for our country, the virus of this type was first reported in the Primorsky, Khabarovsk, Zabaikalsky Krais and Amur Oblast in 2005–2006. The results of the phylogenetic analysis of the recovered isolates demonstrated that FMD emergence in the Subjects of the Russian Federation was attributed to the virus introduction from the neighboring territories. Possible virus introduction from China resulted in significant economic expenditures on FMD containment and eradication. In view of the close trade and economic relations between the Russian Federation and such Asia-Pacific countries as China, India, Japan and Republic of Korea, one should place greater focus on the risk of FMDV introduction into the Russian Federation from these countries. Of key importance is intensification of the international cooperation with the Asian countries in the area of joint activities aimed at FMD freedom maintenance.

Many aspects of the porcine reproductive and respiratory syndrome virus (PRRSV) between‐farm transmission dynamics have been investigated, but uncertainty remains about the significance of farm type and different transmission routes on PRRSV spread. We developed a stochastic epidemiological model calibrated on weekly PRRSV outbreaks accounting for the population dynamics in different pig production phases, breeding herds, gilt development units, nurseries and finisher farms, of three hog producer companies. Our model accounted for indirect contacts by the close distance between farms (local transmission), between‐farm animal movements (pig flow) and reinfection of sow farms (re‐break). The fitted model was used to examine the effectiveness of vaccination strategies and complementary interventions such as enhanced PRRSV detection and vaccination delays and forecast the spatial distribution of PRRSV outbreak. The results of our analysis indicated that for sow farms, 59% of the simulated infections were related to local transmission (e.g. airborne, feed deliveries, shared equipment) whereas 36% and 5% were related to animal movements and re‐break, respectively. For nursery farms, 80% of infections were related to animal movements and 20% to local transmission; while at finisher farms, it was split between local transmission and animal movements. Assuming that the current vaccines are 1% effective in mitigating between‐farm PRRSV transmission, weaned pigs vaccination would reduce the incidence of PRRSV outbreaks by 3%, indeed under any scenario vaccination alone was insufficient for completely controlling PRRSV spread. Our results also showed that intensifying PRRSV detection and/or vaccination pigs at placement increased the effectiveness of all simulated vaccination strategies. Our model reproduced the incidence and PRRSV spatial distribution; therefore, this model could also be used to map current and future farms at‐risk. Finally, this model could be a useful tool for veterinarians, allowing them to identify the effect of transmission routes and different vaccination interventions to control PRRSV spread. [ABSTRACT FROM AUTHOR]

Despite its peculiar distribution, the biology of the southernmost bat species in the world, the Chilean myotis (Myotis chiloensis), has garnered little attention so far. The species has a north-south distribution of c. 2800 km, mostly on the eastern side of the Andes mountain range. Use of extended torpor occurs in the southernmost portion of the range, putting the species at risk of bat white-nose syndrome, a fungal disease responsible for massive population declines in North American bats. Here, we examined how geographic distance and topology would be reflected in the population structure of M. chiloensis along the majority of its range using a double digestion RAD-seq method. We sampled 66 individuals across the species range and discovered pronounced isolation-by-distance. Furthermore, and surprisingly, we found higher degrees of heterozygosity in the southernmost populations compared to the north. A coalescence analysis revealed that our populations may still not have reached secondary contact after the Last Glacial Maximum. As for the potential spread of pathogens, such as the fungus causing WNS, connectivity among populations was noticeably low, especially between the southern hibernatory populations in the Magallanes and Tierra del Fuego, and more northerly populations. This suggests the probability of geographic spread of the disease from the north through bat-to-bat contact to susceptible populations is low. The study presents a rare case of defined population structure in a bat species and warrants further research on the underlying factors contributing to this. See the graphical abstract here. https://doi.org/10.25387/g3.12173385

Abstract Understanding the geographic extent and connectivity of wildlife populations can provide important insights into the management of disease outbreaks but defining patterns of population structure is difficult for widely distributed species. Landscape genetic analyses are powerful methods for identifying cryptic structure and movement patterns that may be associated with spatial epizootic patterns in such cases. We characterized patterns of population substructure and connectivity using microsatellite genotypes from 2,222 white‐tailed deer (Odocoileus virginianus) in the Mid‐Atlantic region of the United States, a region where chronic wasting disease was first detected in 2009. The goal of this study was to evaluate the juxtaposition between population structure, landscape features that influence gene flow, and current disease management units. Clustering analyses identified four to five subpopulations in this region, the edges of which corresponded to ecophysiographic provinces. Subpopulations were further partitioned into 11 clusters with subtle (FST ≤ 0.041), but significant genetic differentiation. Genetic differentiation was lower and migration rates were higher among neighboring genetic clusters, indicating an underlying genetic cline. Genetic discontinuities were associated with topographic barriers, however. Resistance surface modeling indicated that gene flow was diffuse in homogenous landscapes, but the direction and extent of gene flow were influenced by forest cover, traffic volume, and elevational relief in subregions heterogeneous for these landscape features. Chronic wasting disease primarily occurred among genetic clusters within a single subpopulation and along corridors of high landscape connectivity. These results may suggest a possible correlation between population substructure, landscape connectivity, and the occurrence of diseases for widespread species. Considering these factors may be useful in delineating effective management units, although only the largest features produced appreciable differences in subpopulation structure. Disease mitigation strategies implemented at the scale of ecophysiographic provinces are likely to be more effective than those implemented at finer scales.

Predicting how climate warming affects vector borne diseases is a key research priority. The prevailing approach uses the basic reproductive number (R0) to predict warming effects. However, R0 is derived under assumptions of stationary thermal environments; using it to predict disease spread in non-stationary environments could lead to erroneous predictions. Here, we develop a trait-based mathematical model that can predict disease spread and prevalence for any vector borne disease under any type of non-stationary environment. We parameterize the model with trait response data for the Malaria vector and pathogen to test the latest IPCC predictions on warmer-than-average winters and hotter-than-average summers. We report three key findings. First, the R0 formulation commonly used to investigate warming effects on disease spread violates the assumptions underlying its derivation as the dominant eigenvalue of a linearized host-vector model. As a result, it overestimates disease spread in cooler environments and underestimates it in warmer environments, proving its predictions to be unreliable even in a constant thermal environment. Second, hotter-than-average summers both narrow the thermal limits for disease prevalence, and reduce prevalence within those limits, to a much greater degree than warmer-than-average winters, highlighting the importance of hot extremes in driving disease burden. Third, while warming reduces infected vector populations through the compounding effects of adult mortality, and infected host populations through the interactive effects of mortality and transmission, uninfected vector populations prove surprisingly robust to warming. This suggests that ecological predictions of warming-induced reductions in disease burden should be tempered by the evolutionary possibility of vector adaptation to both cooler and warmer climates.

African swine fever (ASF) was diagnosed for the first time in Romania in a backyard holding in Satu‐Mare County in July 2017. Since then, more than 3800 outbreaks occurred in the entire country. Disease control strategies in the backyard sector rely almost exclusively on reactive measures implemented upon appearance of clinical signs and laboratory confirmation of ASF. In our descriptive study, infection course and outbreak investigation data of 56 affected backyard holdings in Satu‐Mare County has been investigated. Early disease detection based on clinical signs appeared to be efficient. In the majority of outbreaks, ASF was detected within the first 2 weeks after the estimated virus introduction. A clinical phase of 2–8 days was observed before pigs either succumbed to the disease or control measures were implemented on affected farms. A moderate on‐farm transmissibility of ASF virus between pigs was observed. Four clusters of outbreaks were identified indicating virus perpetuation and transmission from farm to farm. To suspend infection chains, rapid intervention by isolating affected farms combined with effective biosecurity measures is required. However, due to the backyard peculiarities, quick and effective implementation of control measures has shown to be rather difficult. [ABSTRACT FROM AUTHOR]

The most pervasive and harmful illness is cruciferous black spot disease. This disease starts to show symptoms as soon as the seed emerges from the soil and continues all through the growth season. In regions designated for seeds, cabbages left for storage, and areas planted for human consumption, the disease is seen. The illness Alternaria mostly affects the cruciferous plants’ organs responsible for generating seeds, and it severely damages the pods and seeds. The leaves and bulbs of crucifers are less impacted. Every region that grows cruciferous vegetable crops has Alternaria disease. In this article, the spread and development of alternaria disease in cruciferous vegetable crops (cabbage, cauliflower, broccoli, red cabbage, collard, Napa cabbage) and its damage by the fungus A. brassicae, which causes alternaria disease, and damage by alternaria disease and its causative agent, were determined. Due to the Alternaria disease, the yield loss of all vegetable crops was observed. Compared to a healthy plant, the loss of 14.3-17.4% of the yield of cabbage, followed by cauliflower - 8.3-12.1%, red cabbage - 10.0%, Napa cabbage - 10.0-12.2% and, broccoli 10.2-16.0% was reported.

In this paper, we study the dynamics of phytoplankton–zooplankton system with delay, where delay means that releasing toxin for phytoplankton is not instantaneous. First, we prove the positivity and boundedness of solutions, discuss the Hopf bifurcation caused by delay. Furthermore, we study the property of Hopf bifurcation by center manifold and normal form. Then, we study the global existence of bifurcated periodic solution. Finally by simulation, we show the influence of delay, disease spread and recovery from infected to susceptible on the dynamics of phytoplankton–zooplankton system. [ABSTRACT FROM AUTHOR]