Pathogen Transmission Rates Research Articles

Disease prevention and resistance in social insects: modeling the survival consequences of immunity, hygienic behavior, and colony organization

Understanding the origin of disease resistance in social insects is difficult due to the lack of well-established phylogenies of presocial and eusocial species and the absence of extant basal and intermediate forms. Moreover, comprehensive accounts of infection-control traits in social insect lineages are not available. Therefore, to explore the evolution of pathogen control in social insects we used cellular automata models to analyze the efficacy of immunity and nest hygiene, which we assumed were basal traits, and allogrooming, which likely followed the transition to eusociality, and their interactions with colony demography and patterns of worker spatial distribution. Models showed that nest hygiene provided an immediate survival benefit and that immunity lowered overall disease susceptibility under both constant and periodic exposure scenarios. Allogrooming increased survivorship in chronically challenged colonies but also increased pathogen transmission rates under conditions of periodic exposure. Colonies having demographies biased towards young or old individuals had slightly higher mortality than those with heterogeneous demographies. The distribution of older individuals relative to the nest center had no significant effect on susceptibility and provided only a minor survival advantage. Models indicated that nest hygiene and immunity function on different temporal scales and can interact with demography to lower disease risks. Our results suggest how infection control systems in social insects could have been built upon the inducible immune defenses and nest hygienic behaviors of solitary and presocial ancestors and served as important preadaptations to manage disease exposure and transmission in colonies of eusocial species.

Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data

This paper describes a stochastic epidemic model developed to infer transmission rates of asymptomatic communicable pathogens within a hospital ward. Inference is complicated by partial observation of the epidemic process and dependencies within the data. The epidemic process of nosocomial communicable pathogens can be partially observed by routine swabs testing for the presence of the pathogen. False-negative swab results must be accounted for and make it difficult to ascertain the number of patients who were colonized. Reversible jump Markov chain Monte Carlo methods are used within a Bayesian framework to make inferences about the colonization rates and unknown colonization times. The methods are applied to routinely collected data concerning methicillin-resistant Staphylococcus Aureus in an intensive care unit to estimate the effectiveness of isolation on reducing transmission of the bacterium.

Noninvasive Assessment of Gastrointestinal Parasite Infections in Free-Ranging Primates

Recent evidence of emerging human diseases with origins or likely transmission to humans, or both, that involve primates and a greater recognition of the risk of human pathogen transmission to free-ranging primates have raised awareness of the potential impact of zoonotic pathogen transmission on primate conservation and nonhuman primate and human health. As human population density continues to increase exponentially, speeding the reduction and fragmentation of primate habitats, greater human-primate contact is inevitable and even higher rates of pathogen transmission are likely. Thus interest has grown in collecting baseline data on patterns of parasitic infections in wild primate populations to provide an index of population health and to begin to assess and, to manage disease risks. Primatologists traditionally have been involved with such surveys through noninvasive assessment of gastrointestinal parasites. Unfortunately, previous studies have tended toward divergent methodologies, compromising the potential for longitudinal and comparative work. Here, I provide practical guidelines and standardized methodologies for the noninvasive assessment of gastrointestinal parasites of primates.

Vertical transmission of American foulbrood ( Paenibacillus larvae) in honey bees ( Apis mellifera)

The mode of transmission between hosts (horizontal versus vertical) of disease agents is important for determination of the evolution of virulence in pathogens. For disease management, it is imperative that the epidemiology of the disease is understood and pathogen transmission rates between hosts is a key factor for this understanding. Surprisingly little is known about transmission rates in honey bee pathology. We have studied the rate of vertical transmission of Paenibacillus larvae, the causative agent of American foulbrood (AFB) in honey bee colonies, as colonies reproduce by colony fission (swarming), by culturing for the spores from repetitive samples of adult bees. The results demonstrate vertical pathogen transmission to daughter swarms. The spore density declines over time in both mother colonies and daughter swarms if mother colonies do not exhibit clinical disease symptoms. Occasional positive samples more than a year post swarming, also in daughter swarms, indicate production of infectious spores from diseased larvae, without clinical disease observable by beekeepers, and/or maintenance of infective spores in the hive environment, allowing both horizontal and vertical transmission to be maintained. The results suggest that the virulence of AFB, being lethal at colony level in contrast to other bee diseases shaped by evolution, could be dependent on apicultural practices and that the pathogen probably would be maintained without causing frequent colony mortality in a natural system.

Use of partial budgeting to determine the economic benefits of antibiotic treatment of chronic subclinical mastitis caused by Streptococcus uberis or Streptococcus dysgalactiae

The economic effect of lactational antibiotic treatment of chronic subclinical intramammary infections due to Streptococcus uberis or Streptococcus dysgalactiae was explored by means of partial budgeting. Effects at cow level and herd level were modelled, including prevention of clinical mastitis episodes and the prevention of transmission of infections. Input variables for our deterministic model were derived from literature or based on 2002/2003 dairy prices and farming conditions in The Netherlands. Sensitivity analysis was used to examine the effect of uncertainty around input variables or changes in price estimates. On farms where pathogen transmission was prevented through proper udder health management, 3-d antibiotic treatment during lactation resulted in an average net profit of euro+11.62 over no treatment while 8-d antibiotic treatment had an average negative net result of euro-21.83. Sensitivity analysis showed that profitability depends on the probability of treatment-induced cure, pathogen transmission rates, culling rate, retention pay-off, and costs of antibiotic treatment. Three-day antibiotic treatment of chronic subclinical streptococcal mastitis is economically profitable over a range of input values for cure probabilities, transmission rates and losses due to culling. In contrast, 8-d lactational treatment is only profitable for very valuable animals, on farms where the risk of pathogen transmission is high and/or when the farmer is likely to cull a high percentage of cows with subclinical mastitis. Because bacterial flora, cow characteristics and management differ widely between farms, the economic outcome of lactational treatment of chronic subclinical streptococcal mastitis may be highly farm-dependent.

Recombination and Migration of Cryphonectria hypovirus 1 as Inferred From Gene Genealogies and the Coalescent

Abstract Genealogy-based methods were used to estimate migration of the fungal virus Cryphonectria hypovirus 1 between vegetative compatibility types of the host fungus, Cryphonectria parasitica, as a means of estimating horizontal transmission within two host populations. Vegetative incompatibility is a self/non-self recognition system that inhibits virus transmission under laboratory conditions but its effect on transmission in nature has not been clearly demonstrated. Recombination within and among different loci in the virus genome restricted the genealogical analyses to haplotypes with common mutation and recombinational histories. The existence of recombination necessitated that we also use genealogical approaches that can take advantage of both the mutation and recombinational histories of the sample. Virus migration between populations was significantly restricted. In contrast, estimates of migration between vegetative compatibility types were relatively high within populations despite previous evidence that transmission in the laboratory was restricted. The discordance between laboratory estimates and migration estimates from natural populations highlights the challenges in estimating pathogen transmission rates. Genealogical analyses inferred migration patterns throughout the entire coalescent history of one viral region in natural populations and not just recent patterns of migration or laboratory transmission. This application of genealogical analyses provides markedly stronger inferences on overall transmission rates than laboratory estimates do.

Host population density and body mass as determinants of species richness in parasite communities: comparative analyses of directly transmitted nematodes of mammals

Epidemiological theory predicts positive correlations between host population density or body mass and species richness among parasite communities. Here I test these predictions by a comparative study of communities of directly transmitted mammalian parasites, gastrointestinal strongylid nematodes. I use data from 45 species of mammals, representing examination of 17 200 individual hosts. The variable studied was the average number of gastrointestinal strongylid nematode species per host population, and three different methods were used to obtain estimates of parasite species richness that are unbiased by number of host individuals examined. Analyses were done using the phylogenetically independent contrast method. Host population density and parasite species richness were strongly positively correlated when the effects of host body weight had been controlled for. Controlling for other variables did not change this, and the relationship was found regardless of method used to correct for uneven sampling effort among host species. A positive relationship between parasite species richness and host body weight was also found, but the effect of host densities had to be controlled for to see this. These relationships between host traits and species richness of directly transmitted parasites are stronger than patterns found using data on indirectly transmitted mammalian parasites, and suggests that links between host traits and parasite species richness are stronger than previously suggested. The results are consistent with parasite species richness being positively linked to pathogen transmission rates and reductions in transmission rates possibly increasing extinction probabilities in parasite populations. The results also suggest that parasites may exert a cost of increases in rate of population energy usage, and thus show that pathogens may be important in generating independence between body mass and rate of population energy usage among host species.

Genetic Improvement of Resistance to Infectious Diseases in Livestock

Despite traditional disease control measures, losses attributable to infectious diseases continue to impede the livestock industries. The emergence of previously undescribed pathogens has been a feature of the end of this century. Increased global travel and semen exchange has contributed to the dissemination of pathogens previously confined to specific regions. In addition, it is now clear that bacterial pathogens cause diseases previously thought not to be infectious. Old diseases have returned, accompanied by the emergence of antibiotic-resistant strains. No new class of antibiotics has been discovered in the past three decades, and derivatives of current antibiotics soon encounter resistance. Other methods are thus desperately needed to counter diseases previously treated by conventional antibiotics. One approach is to improve genetic resistance to infectious pathogens. Evidently, selection for the most resistant animals necessitates the understanding of the components of the host response that lead to elimination of the invading pathogen and resolution of disease. But, it requires also the identification and characterization of the virulence factors and the in vivo survival mechanisms of the invading pathogen.The functional relationship between the pathogen transmission rate and its virulence within- and between-hosts should also be considered. Finally, it must be recognized that current host-pathogen relationships are shaped by coevolutionary mechanisms between host defense mechanisms and pathogen genetic diversity. It is therefore distressing that studies dealing with infectious disease have developed until now separately. The joint efforts of many different teams with complementary expertise are necessary to evaluate simultaneously the impact of all disease determinants and of their interaction on natural resistance to infectious disease.

An age-structured population model of citrus rust mite: a fruit–mite–fungal pathogen system

An age-structured model of a fruit–mite–fungal pathogen system was developed to study interactions between the `Hamlin' orange fruit, the citrus rust mite (CRM) Phyllocoptruta oleivora, and its fungal pathogen Hirsutella thompsonii. The model consists of a set of difference equations incorporating the age and stage change of CRM and its fungal pathogen. The abiotic factors included in the model were daily mean temperature and daily dew period the biotic factors were mite density (egg, protonymph, deutonymph, and adult), pathogen density (latent pathogen, and infectious pathogen), and undamaged fruit surface area. One data set for the fruit–mite–pathogen dynamics was used to estimate parameters related to density-dependent CRM egg production and pathogen transmission rate. Parameters were estimated by choosing the parameter combinations which gave the least combined error sum of squares between observed and simulated populations for CRM and pathogen. For the data set used for parameter estimation, the model captured 92.2% of variation in CRM population dynamics, and 61.9% of variation in the density curve pattern of the pathogen, over a period of 5 months. Three additional data sets were used to partially test model predictability. For one data set, the model captured 87.1% of variation in CRM population dynamics, and 94.4% of variation in the density curve pattern of the pathogen, over a period of 5 months. The model, however, did not make accurate predictions for low CRM density level-pathogen interactions. The corresponding cumulative mite days and fruit surface damage were also generated by the model for each data set. With further modification and refinement, the model could be developed into a useful tool for CRM management.

Improved Estimation of Pathogen Transmission Rates by Group Testing

Improved Estimation of Pathogen Transmission Rates by Group Testing

Shortage of junior staff

<h3>Abstract</h3> The implementation of non-pharmaceutical public health interventions can have simultaneous impacts on pathogen transmission rates as well as host mobility rates. For instance, with SARS-CoV-2, masking can influence host-to-host transmission, while stay-at-home orders can influence mobility. Importantly, transmission rates and mobility patterns can influence pathogen-induced hospitalization rates. This poses a significant challenge for the use of mathematical models of disease dynamics in forecasting the spread of a pathogen; to create accurate forecasts in spatial models of disease spread, we must simultaneously account for time-varying rates of transmission and host movement. In this study, we develop a statistical model-fitting algorithm to estimate dynamic rates of SARS-CoV-2 transmission and host movement from geo-referenced hospitalization data. Using simulated data sets, we then test whether our method can accurately estimate these time-varying rates simultaneously, and how this accuracy is influenced by the spatial population structure. Our model-fitting method relies on a highly parallelized process of grid search and a sliding window technique that allows us to estimate time-varying transmission rates with high accuracy and precision, as well as movement rates with somewhat lower precision. Estimated parameters also had lower precision in more rural data sets, due to lower hospitalization rates (i.e., these areas are less data-rich). This model-fitting routine could easily be generalized to any stochastic, spatially-explicit modeling framework, offering a flexible and efficient method to estimate time-varying parameters from geo-referenced data sets.