cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8152841
title: Anatomopathological changes, quantification and distribution of <i>Libyostrongylus</i> spp. in regions of the proventriculus and ventriculus of naturally- and experimentally-infected ostriches

description: Nematodes of the genus <i>Libyostrongylus</i> parasitize ostriches, causing high mortality rates. These nematodes are found in the proventriculus and ventriculus of ostriches, but little is known about their distribution and the possible anatomopathological changes they cause in the various regions of these organs. This paper describes the distribution and quantification of <i>Libyostrongylus</i> and pathological changes found in regions of the proventriculus and ventriculus of ostriches with high and low levels of both natural and experimental infection. Ostriches were necropsied and tissue samples from the distinct regions of both organs were analysed based on nematode counts and histopathology after staining with haematoxylin and eosin, Masson’s trichrome or Alcian blue/PAS. The cranial and glandular regions of the proventriculus were the most parasitized. The ventriculus contained more nematodes in the caudal region. No macro- or microscopic pathological changes were observed in either of these organs of experimentally-infected birds. However, naturally-infected birds with high levels of infection presented proventriculus with macroscopic lesions and heterophilic infiltrates surrounding nematodes. In the glandular region of this organ, nematodes were located in the adenomeres of the secretory ducts, causing altered architecture and erosions and ulcerative lesions with damaged epithelium. Nematode eggs were found in the koilin layer of the middle and caudal regions of the ventriculus only of these birds. The pH of the regions assessed by Alcian blue/PAS staining changed from acidic in the proventriculus to more alkaline in the caudal region of the ventriculus. These data add knowledge to the biology of <i>Libyostrongylus</i>. <b>RESEARCH HIGHLIGHTS</b>The most parasitized areas were the cranial and glandular regions of the proventriculus.Naturally-infected birds with high levels of infection presented macro lesions in the proventriculus and damaged epithelium.Nematode eggs were found in the ventriculus.The proventriculus had an acidic pH, which turned alkaline towards the ventriculus. The most parasitized areas were the cranial and glandular regions of the proventriculus. Naturally-infected birds with high levels of infection presented macro lesions in the proventriculus and damaged epithelium. Nematode eggs were found in the ventriculus. The proventriculus had an acidic pH, which turned alkaline towards the ventriculus.
DOI: 10.6084/m9.figshare.8152841.v1
title: Anatomopathological changes, quantification and distribution of <i>Libyostrongylus</i> spp. in regions of the proventriculus and ventriculus of naturally- and experimentally-infected ostriches

description: Nematodes of the genus <i>Libyostrongylus</i> parasitize ostriches, causing high mortality rates. These nematodes are found in the proventriculus and ventriculus of ostriches, but little is known about their distribution and the possible anatomopathological changes they cause in the various regions of these organs. This paper describes the distribution and quantification of <i>Libyostrongylus</i> and pathological changes found in regions of the proventriculus and ventriculus of ostriches with high and low levels of both natural and experimental infection. Ostriches were necropsied and tissue samples from the distinct regions of both organs were analysed based on nematode counts and histopathology after staining with haematoxylin and eosin, Masson’s trichrome or Alcian blue/PAS. The cranial and glandular regions of the proventriculus were the most parasitized. The ventriculus contained more nematodes in the caudal region. No macro- or microscopic pathological changes were observed in either of these organs of experimentally-infected birds. However, naturally-infected birds with high levels of infection presented proventriculus with macroscopic lesions and heterophilic infiltrates surrounding nematodes. In the glandular region of this organ, nematodes were located in the adenomeres of the secretory ducts, causing altered architecture and erosions and ulcerative lesions with damaged epithelium. Nematode eggs were found in the koilin layer of the middle and caudal regions of the ventriculus only of these birds. The pH of the regions assessed by Alcian blue/PAS staining changed from acidic in the proventriculus to more alkaline in the caudal region of the ventriculus. These data add knowledge to the biology of <i>Libyostrongylus</i>. <b>RESEARCH HIGHLIGHTS</b>The most parasitized areas were the cranial and glandular regions of the proventriculus.Naturally-infected birds with high levels of infection presented macro lesions in the proventriculus and damaged epithelium.Nematode eggs were found in the ventriculus.The proventriculus had an acidic pH, which turned alkaline towards the ventriculus. The most parasitized areas were the cranial and glandular regions of the proventriculus. Naturally-infected birds with high levels of infection presented macro lesions in the proventriculus and damaged epithelium. Nematode eggs were found in the ventriculus. The proventriculus had an acidic pH, which turned alkaline towards the ventriculus.
DOI: 10.6084/m9.figshare.12865880
title: Phasianus colchicus gene list

description: A tab delimited file of the common pheasant gene that generated by using EVM.
DOI: 10.6084/m9.figshare.8152811
title: Hysteresis.f

description: Ising model fortran
DOI: 10.6084/m9.figshare.8152811.v1
title: Hysteresis.f

description: Ising model fortran
DOI: 10.6084/m9.figshare.8152805
title: Magnitudes.f

description: Ising model fortran
DOI: 10.6084/m9.figshare.8152805.v1
title: Magnitudes.f

description: Ising model fortran
DOI: 10.6084/m9.figshare.8152802
title: Comprehensive RData archive of probabilistic forecasts from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152802.v1
title: Comprehensive RData archive of probabilistic forecasts from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152799
title: Log scores for probabilistic forecasts across all age group, seasons, weeks and targets from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152799.v1
title: Log scores for probabilistic forecasts across all age group, seasons, weeks and targets from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152793
title: R script to analyze data and generate tables/plots used in the manuscript from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152793.v1
title: R script to analyze data and generate tables/plots used in the manuscript from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152790
title: Forecasts and metrics for baseline method from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152790.v1
title: Forecasts and metrics for baseline method from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.
DOI: 10.6084/m9.figshare.8152787
title: Comprehensive RData archive of point forecasts from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

description: Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.