scholarly journals Partially observed epidemics in wildlife hosts: modelling an outbreak of dolphin morbillivirus in the northwestern Atlantic, June 2013–2014

2015 ◽  
Vol 12 (112) ◽  
pp. 20150676 ◽  
Author(s):  
Sinead E. Morris ◽  
Jonathan L. Zelner ◽  
Deborah A. Fauquier ◽  
Teresa K. Rowles ◽  
Patricia E. Rosel ◽  
...  
Keyword(s):  
Marine Mammals ◽  
Process Model ◽  
Transmission Rate ◽  
Virus Transmission ◽  
Seasonal Migration ◽  
Spatial Spread ◽  
Viral Spread ◽  
Statistical Framework ◽  
Dolphin Morbillivirus ◽  
Spatio Temporal

Morbilliviruses cause major mortality in marine mammals, but the dynamics of transmission and persistence are ill understood compared to terrestrial counterparts such as measles; this is especially true for epidemics in cetaceans. However, the recent outbreak of dolphin morbillivirus in the northwestern Atlantic Ocean can provide new insights into the epidemiology and spatio-temporal spread of this pathogen. To deal with uncertainties surrounding the ecology of this system (only stranded animals were observed), we develop a statistical framework that can extract key information about the underlying transmission process given only sparse data. Our self-exciting Poisson process model suggests that individuals are infectious for at most 24 days and can transfer infection up to two latitude degrees (220 km) within this time. In addition, the effective reproduction number is generally below one, but reaches 2.6 during a period of heightened stranding numbers near Virginia Beach, Virginia, in summer 2013. Network analysis suggests local movements dominate spatial spread, with seasonal migration facilitating wider dissemination along the coast. Finally, a low virus transmission rate or high levels of pre-existing immunity can explain the lack of viral spread into the Gulf of Mexico. More generally, our approach illustrates novel methodologies for analysing very indirectly observed epidemics.

Statistical-based dynamic machine learning models for nonlinear spatio-temporal processes

2018 ◽  
Author(s):  
◽  
Patrick McDermott
Keyword(s):  
Machine Learning ◽  
Uncertainty Quantification ◽  
Process Model ◽  
Point Estimation ◽  
Dynamical Process ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Statistical Framework ◽  
Temporal Models ◽  
Spatio Temporal

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] One of the most vital aspects of any spatio-temporal model is characterizing the dynamics of the process. In both a spatio-temporal forecasting and data assimilation setting, the dynamical process model often determines the success of a given methodology. Although in some cases mechanistic processes can motivate a dynamical process model, frequently the dynamics involve complex nonlinear behavior that is difficult to specify a priori. This nonlinearity is generally induced by interactions between the process at various spatial locations or between various scales of variability. Over the past few decades, machine learning methods have been shown to be successful for predicting or classifying high-dimensional complex nonlinear multivariate processes. Even though spatio-temporal processes share many of these same characteristics, only recently have machine learning methods been applied to spatio-temporal processes. Furthermore, uncertainty quantification is almost always ignored in the machine learning literature, with a focus only on point estimation prediction or classification. The computational burden of implementing these models within an uncertainty quantification framework often discourages the use of a more traditional statistical framework. Yet, uncertainty quantification is critical for nonlinear spatio-temporal processes where unique behaviors such as non-Gaussianity and extremes are often found. This dissertation focuses on retaining components of machine learning methods for dynamical processes that have been important for their success, while placing them within a formal statistical framework that is computationally feasible. Specifically, a Bayesian framework is employed so the uncertainties associated with the investigated processes can rigorously be quantified. The developed Bayesian machine learning based statistical dynamical spatio-temporal models are shown to outperform a suite of competing methods in terms of both forecast accuracy and uncertainty quantification for multiple nonlinear spatio-temporal processes.

scholarly journals Dynamics of the COVID-19 epidemic in Ireland under mitigation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bernard Cazelles ◽  
Benjamin Nguyen-Van-Yen ◽  
Clara Champagne ◽  
Catherine Comiskey
Keyword(s):  
Transmission Rate ◽  
Vaccination Campaign ◽  
Virus Transmission ◽  
Reporting Rate ◽  
Mitigation Measures ◽  
Third Wave ◽  
The European Union ◽  
Hospital Data ◽  
Prevalence Survey ◽  
Epidemic Curve

Abstract Background In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination. Methods To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data. Results In Ireland, mitigation measures provided a 78–86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2–3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved. Conclusion We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

scholarly journals Examining the interplay between face mask usage, asymptomatic transmission, and social distancing on the spread of COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Catching ◽  
Sara Capponi ◽  
Ming Te Yeh ◽  
Simone Bianco ◽  
Raul Andino
Keyword(s):  
Social Distance ◽  
Large Fraction ◽  
Real Life ◽  
Virus Transmission ◽  
Face Mask ◽  
Social Distancing ◽  
Viral Spread ◽  
The Face ◽  
Asymptomatic Individuals ◽  
High Virus

AbstractCOVID-19’s high virus transmission rates have caused a pandemic that is exacerbated by the high rates of asymptomatic and presymptomatic infections. These factors suggest that face masks and social distance could be paramount in containing the pandemic. We examined the efficacy of each measure and the combination of both measures using an agent-based model within a closed space that approximated real-life interactions. By explicitly considering different fractions of asymptomatic individuals, as well as a realistic hypothesis of face masks protection during inhaling and exhaling, our simulations demonstrate that a synergistic use of face masks and social distancing is the most effective intervention to curb the infection spread. To control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance. Finally, the face mask effectiveness in curbing the viral spread is not reduced if a large fraction of population is asymptomatic. Our findings have important implications for policies that dictate the reopening of social gatherings.

scholarly journals A revision of daily egg production estimation methods, with application to Atlanto-Iberian sardine. 2. Spatially and environmentally explicit estimates of egg production

2011 ◽  
Vol 68 (3) ◽  
pp. 528-536 ◽  
Author(s):  
Miguel Bernal ◽  
Yorgos Stratoudakis ◽  
Simon Wood ◽  
Leire Ibaibarriaga ◽  
Luis Valdés ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Environmental Variables ◽  
Egg Production ◽  
Stock Assessment ◽  
Production Model ◽  
Estimation Methods ◽  
Statistical Framework ◽  
Spatio Temporal ◽  
The Relationship ◽  
Made In

Abstract Bernal, M., Stratoudakis, Y., Wood, S., Ibaibarriaga, L., Uriarte, A., Valdés, L., and Borchers, D. 2011. A revision of daily egg production estimation methods, with application to Atlanto-Iberian sardine. 2. Spatially and environmentally explicit estimates of egg production. – ICES Journal of Marine Science, 68: . A spatially and environmentally explicit egg production model is developed to accommodate a number of assumptions about the relationship between egg production and mortality and associated environmental variables. The general model was tested under different assumptions for Atlanto-Iberian sardine. It provides a flexible estimator of egg production, in which a range of assumptions and hypotheses can be tested in a structured manner within a well-defined statistical framework. Application of the model to Atlanto-Iberian sardine increased the precision of the egg production time-series, and allowed improvements to be made in understanding the spatio-temporal variability in egg production, as well as implications for ecology and stock assessment.

scholarly journals Genomic epidemiology reveals multiple introductions of SARS-CoV-2 followed by community and nosocomial spread, Germany, February to May 2020

2021 ◽  
Vol 26 (43) ◽  
Author(s):  
Maximilian Muenchhoff ◽  
Alexander Graf ◽  
Stefan Krebs ◽  
Caroline Quartucci ◽  
Sandra Hasmann ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Local Level ◽  
University Hospital ◽  
Metropolitan Region ◽  
Viral Genomes ◽  
Genomic Epidemiology ◽  
Viral Spread ◽  
Spatio Temporal

Background In the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data. Aim We applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata. Methods We investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission. Results We identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high (9.6% and 54%, respectively) and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions. Conclusions Early spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies.

scholarly journals A Spatio-Temporal Investigation of the Growth Rate of COVID-19 Incidents in Ohio, USA, Early in the Pandemic

2021 ◽  
Vol 121 (2) ◽  
pp. 33-47
Author(s):  
Alessandro M. Selvitella ◽  
Liam Carolan ◽  
Justin Smethers ◽  
Christopher Hernandez ◽  
Kathleen L. Foster
Keyword(s):  
Growth Rate ◽  
Transmission Rate ◽  
Time Lag ◽  
Early Period ◽  
Mitigation Strategies ◽  
Initial Growth ◽  
School Closures ◽  
Policy Makers ◽  
Susceptible Population ◽  
Spatio Temporal

Understanding the initial growth rate of an epidemic is important for epidemiologists and policy makers as it can impact their mitigation strategies such as school closures, quarantines, or social distancing. Because the transmission rate depends on the contact rate of the susceptible population with infected individuals, similar growth rates might be experienced in nearby geographical areas. This research determined the growth rate of cases and deaths associated with COVID-19 in the early period of the 2020 pandemic in Ohio, United States. The evolution of cases and deaths was modeled through a Besag-York-Molliè model with linear- and power-type deterministic time dependence. The analysis showed that the growth rate of the time component of the model was subexponential in both cases and deaths once the time-lag across counties of the appearance of the first COVID-19 case was considered. Moreover, deaths in the northeast counties in Ohio were strongly related to the deaths in nearby counties.

scholarly journals Key Factors Influencing the Incidence of West Nile Virus in Burleigh County, North Dakota

2018 ◽  
Vol 15 (9) ◽  
pp. 1928 ◽  
Author(s):  
Hiroko Mori ◽  
Joshua Wu ◽  
Motomu Ibaraki ◽  
Franklin Schwartz
Keyword(s):  
West Nile Virus ◽  
North Dakota ◽  
Transmission Rate ◽  
Virus Transmission ◽  
Correlation Coefficients ◽  
West Nile ◽  
Mosquito Abundance ◽  
River Flooding ◽  
Disease Case ◽  
The City

The city of Bismarck, North Dakota has one of the highest numbers of West Nile Virus (WNV) cases per population in the U.S. Although the city conducts extensive mosquito surveillance, the mosquito abundance alone may not fully explain the occurrence of WNV. Here, we developed models to predict mosquito abundance and the number of WNV cases, independently, by statistically analyzing the most important climate and virus transmission factors. An analysis with the mosquito model indicated that the mosquito numbers increase during a warm and humid summer or after a severely cold winter. In addition, river flooding decreased the mosquito numbers. The number of WNV cases was best predicted by including the virus transmission rate, the mosquito numbers, and the mosquito feeding pattern. This virus transmission rate is a function of temperature and increases significantly above 20 °C. The correlation coefficients (r) were 0.910 with the mosquito-population model and 0.620 with the disease case model. Our findings confirmed the conclusions of other work on the importance of climatic variables in controlling the mosquito numbers and contributed new insights into disease dynamics, especially in relation to extreme flooding. It also suggested a new prevention strategy of initiating insecticides not only based on mosquito numbers but also 10-day forecasts of unusually hot weather.

scholarly journals Saprophytic Activity and Sporulation of Cryphonectria parasitica on Dead Chestnut Wood in Forests with Naturally Established Hypovirulence

2006 ◽  
Vol 96 (12) ◽  
pp. 1337-1344 ◽  
Author(s):  
S. Prospero ◽  
M. Conedera ◽  
U. Heiniger ◽  
D. Rigling
Keyword(s):  
Biological Control ◽  
Dead Wood ◽  
Transmission Rate ◽  
Virus Transmission ◽  
Cryphonectria Parasitica ◽  
Chestnut Blight ◽  
Chestnut Blight Fungus ◽  
The Mean ◽  
Chestnut Wood

Sustainable biological control of the chestnut blight fungus Crypho-nectria parasitica with hypovirulence depends on the production and dissemination of hypovirus-infected propagules of the pathogen. We investigated the ability of C. parasitica to sporulate and produce hypo-virus-infected spores on recently dead chestnut wood in coppice stands in southern Switzerland where hypovirulence has been naturally established. The number and type (active, inactive, or none) of cankers was assessed on experimentally cut and stacked stems, firewood stacks, and natural dead wood. Hypovirus-free and hypovirus-infected strains readily survived for more than 1 year in the chestnut blight cankers of the stacked stems. Sporulation of C. parasitica was observed on the surface of preexisting inactive and active cankers, as well as on newly colonized bark areas and was significantly more abundant than on comparable cankers on living stems. On all types of dead wood, we observed more stromata with perithecia than with pycnidia; however, a large proportion of the stromata was not differentiated. All perithecia examined yielded only hypovirus-free ascospores. The incidence of pycnidia that produced hypovirus-infected conidia ranged from 5% on natural dead wood to 41% on the experimental stacks. The mean virus transmission rate into conidia was 69%. Our study demonstrates a considerable saprophytic activity of C. parasitica on recently dead chestnut wood and supports the hypothesis of a role of this saprophytic phase in the epidemiology of hypovirulence.

scholarly journals Citrus tristeza virus P33 Protein Is Required for Efficient Transmission by the Aphid Aphis (Toxoptera) citricidus (Kirkaldy)

Viruses ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1131
Author(s):  
Turksen Shilts ◽  
Choaa El-Mohtar ◽  
William O. Dawson ◽  
Nabil Killiny
Keyword(s):  
Citrus Tristeza Virus ◽  
Transmission Rate ◽  
Control Strategies ◽  
Virus Transmission ◽  
Plant Viruses ◽  
Transmission Mechanisms ◽  
Infectious Clones ◽  
Underlying Mechanisms ◽  
Tristeza Virus ◽  
Citrus Tristeza

Plant viruses are threatening many valuable crops, and Citrus tristeza virus (CTV) is considered one of the most economically important plant viruses. CTV has destroyed millions of citrus trees in many regions of the world. Consequently, understanding of the transmission mechanism of CTV by its main vector, the brown citrus aphid, Aphis (Toxoptera) citricidus (Kirkaldy), may lead to better control strategies for CTV. The objective of this study was to understand the CTV–vector relationship by exploring the influence of viral genetic diversity on virus transmission. We built several infectious clones with different 5′-proximal ends from different CTV strains and assessed their transmission by the brown citrus aphid. Replacement of the 5′- end of the T36 isolate with that of the T30 strain (poorly transmitted) did not increase the transmission rate of T36, whereas replacement with that of the T68-1 isolate (highly transmitted) increased the transmission rate of T36 from 1.5 to 23%. Finally, substitution of p33 gene of the T36 strain with that of T68 increased the transmission rate from 1.5% to 17.8%. Although the underlying mechanisms that regulate the CTV transmission process by aphids have been explored in many ways, the roles of specific viral proteins are still not explicit. Our findings will improve our understanding of the transmission mechanisms of CTV by its aphid vector and may lead to the development of control strategies that interfere with its transmission by vector.

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