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Rabies, a multi-host pathogen responsible for the loss of roughly 59,000 human lives each year worldwide, continues to impose a significant burden of disease despite control efforts, especially in Ethiopia. However, how species other than dogs contribute to rabies transmission throughout Ethiopia remains largely unknown. In this study, we quantified interactions among wildlife species in Ethiopia with the greatest potential for contributing to rabies maintenance. We observed wildlife at supplemental scavenging sites across multiple landscape types and quantified transmission potential. More specifically, we used camera trap data to quantify species abundance, species distribution, and intra- and inter-species contacts per trapping night over time and by location. We derived a mathematical expression for the basic reproductive number (R0 ) based on within- and between-species contract rates by applying the next generation method to the susceptible, exposed, infectious, removed model. We calculated R0 for transmission within each species and between each pair of species using camera trap data in order to identify pairwise interactions that contributed the most to transmission in an ecological community. We estimated which species, or species pairs, could maintain transmission ( R 0 > 1 ${R_0} > 1$ ) and which species, or species pairs, had contact rates too low for maintenance ( R 0 < 1 ${R_0} < 1$ ). Our results identified multiple urban carnivores as candidate species for rabies maintenance throughout Ethiopia, with hyenas exhibiting the greatest risk for rabies maintenance through intra-species transmission. Hyenas and cats had the greatest risk for rabies maintenance through inter-species transmission. Urban and peri-urban sites posed the greatest risk for rabies transmission. The night-time hours presented the greatest risk for a contact event that could result in rabies transmission. Overall, both intra- and inter-species contacts posed risk for rabies maintenance. Our results can be used to target future studies and inform population management decisions.

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Traditional compartmental models such as SIR (susceptible, infected, recovered) assume that the epidemic transmits in a homogeneous population, but the real contact patterns in epidemics are heterogeneous. Employing a more realistic model that considers heterogeneous contact is consequently necessary. Here, we use a contact network to reconstruct unprotected, protected contact, and airborne spread to simulate the two-stages outbreak of COVID-19 (coronavirus disease 2019) on the "Diamond Princess" cruise ship. We employ Bayesian inference and Metropolis-Hastings sampling to estimate the model parameters and quantify the uncertainties by the ensemble simulation technique. During the early epidemic with intensive social contacts, the results reveal that the average transmissibility t was 0.026 and the basic reproductive number R 0 was 6.94, triple that in the WHO report, indicating that all people would be infected in one month. The t and R 0 decreased to 0.0007 and 0.2 when quarantine was implemented. The reconstruction suggests that diluting the airborne virus concentration in closed settings is useful in addition to isolation, and high-risk susceptible should follow rigorous prevention measures in case exposed. This study can provide useful implications for control and prevention measures for the other cruise ships and closed settings.

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How a disease is transmitted affects our ability to determine R0, the average number of new cases caused by an infectious host at the onset of an epidemic. R0 becomes progressively more difficult to compute as transmission varies from directly transmitted diseases to diseases that are vector-borne to environmentally transmitted diseases. Pathogens responsible for diseases with environmental transmission are typically maintained in environmental reservoirs that exhibit a complex spatial distribution of local infectious zones (LIZs). Understanding host encounters with LIZs and pathogen persistence within LIZs is required for an accurate R0 and modeling these contacts requires an integrated geospatial and dynamical systems approach. Here we review how interactions between host and pathogen populations and environmental reservoirs are driven by landscape-level variables, and synthesize the quantitative framework needed to formulate outbreak response and disease control.

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Clinicians can evaluate the relevance of an outbreak based on its basic reproductive number (R0). So far there has been no report on the R0 of Mycoplasma conjunctivae which is a major cause of goats' conjunctivitis in Taiwan. The present study sought to investigate an outbreak of infectious keratoconjunctivitis (IKC) by Mycoplasma conjunctivae (MC) in an indoor dairy goat barn. The epidemiological curve was recorded to build a susceptible-infected-recovered model and to estimate the R0 by three methods In the investigated goat barn, 60% (31/55) goats showed degrees of IKC signs. The number of infected animals increased quickly after 15days, but slowed down after 41days. The sick goats began to recover after 30days. The epidemic fully stopped after 81days. The estimated R0 ranged from 1.35 to 4.46. In summary, this is the first MC report in Taiwan, and the first one to estimate the R0 of MC.

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