Local Temporal Trends Research Articles

Systematic changes and random variations: Understanding lake trout (Salvelinus namaycush) growth dynamics in US waters of Lake Huron

Changes in fish length-at-age can be modeled by adding the age, year-class, and year effects together, or tracking cohort-specific growth with time-dependent parameters. We implemented both approaches to better understand lake trout (Salvelinus namaycush) length-at-age variations in US waters of Lake Huron, 1977–2019. We directly compared length-at-age over time using our linear-mixed model, where the expected length-at-age on logscale was represented by the sum of a fixed age effect, a fixed year-class effect (same for all ages of a cohort), and a random year effect (same for all ages within a year). The age, year-class, and year effects together indicated a general decline in length-at-age between the two time-periods of 1977–1997 and 1998–2019. The random year effect had local temporal trends that differed between the two time-periods, while the pattern of decreases in the fixed year-class effect appeared to reflect changes in growth conditions that were represented eventually by the largest prey-biomass decline during 1995–1996 and the final alewife collapse in 2003. We also predicted annual averages of length-at-age by tracking cohort-specific growth histories. We modified the Ford-Walford plot to illustrate how the growth processes are both cohort-specific and time-dependent. The estimates of year-specific asymptotic length indicated the decline in lake trout growth potential between the two time-periods specified above. Our two models differed fundamentally in their statistical assumptions, but they both fitted the data well and explained the growth decline between the two time-periods, which was also consistent with our understanding of the trophic shift in the ecosystem.

COVID-19 geoviz for spatio-temporal structures detection

Abstract. The spread of COVID-19 has motivated a wide interest in visualization tools to represent the pandemic’s spatio-temporal evolution. This tools usually rely on dashboard environments which depict COVID-19 data as temporal series related to different indicators (number of cases, deaths) calculated for several spatial entities at different scales (countries or regions). In these tools, diagrams (line charts or histograms) display the temporal component of data, and 2D cartographic representations display the spatial distribution of data at one moment in time. In this paper, we aim at proposing novel visualization designs in order to help medical experts to detect spatio-temporal structures such as clusters of cases and spatial axes of propagation of the epidemic, through a visual analysis of detailed COVID-19 event data. In this context, we investigate and revisit two visualizations, one based on the Growth Ring Map technique and the other based on the space-time cube applied on a spatial hexagonal grid. We assess the potential of these visualizations for the visual analysis of COVID-19 event data, through two proofs of concept using synthetic cases data and web-based prototypes. The Grow Ring Map visualization appears to facilitate the identification of clusters and propagation axes in the cases distribution, while the space-time cube appears to be suited for the identification of local temporal trends.

Regional and Local Temporal Trends of Borrelia burgdorferi and Anaplasma spp. Seroprevalence in Domestic Dogs: Contiguous United States 2013-2019.

In 2019, in the United States, over 220,000 and 350,000 dogs tested positive for exposure to Anaplasma spp. and Borrelia burgdorferi, respectively. To evaluate regional and local temporal trends of pathogen exposure we used a Bayesian spatio-temporal binomial regression model, analyzing serologic test results for these pathogens from January 2013 to December 2019. Regional trends were not static over time, but rather increased within and beyond the borders of historically endemic regions. Increased seroprevalence was observed as far as North Carolina and North Dakota for both pathogens. Local trends were estimated to evaluate the heterogeneity of underlying changes. A large cluster of counties with increased B. burgdorferi seroprevalence centered around West Virginia, while a similar cluster of counties with increased Anaplasma spp. seroprevalence centered around Pennsylvania and extended well into Maine. In the Midwest, only a small number of counties experienced an increase in seroprevalence; instead, most counties had a decrease in seroprevalence for both pathogens. These trends will help guide veterinarians and pet owners in adopting the appropriate preventative care practices for their area. Additionally, B. burgdorferi and A. phagocytophilum cause disease in humans. Dogs are valuable sentinels for some vector-borne pathogens, and these trends may help public health providers better understand the risk of exposure for humans.

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.

Local and regional temporal trends (2013\u20132019) of canine Ehrlichia spp. seroprevalence in the USA

BackgroundIn the USA, there are several Ehrlichia spp. of concern including Ehrlichia canis, Ehrlichia ewingii, Ehrlichia chaffeensis, Ehrlichia muris eauclarensis, and “Panola Mountain Ehrlichia”. Of these, E. canis is considered the most clinically relevant for domestic dogs, with infection capable of causing acute, subclinical, and chronic stages of disease. Changes in climate, land use, habitats, and wildlife reservoir populations, and increasing contact between both human and dog populations with natural areas have resulted in the increased risk of vector-borne disease throughout the world.MethodsA Bayesian spatio-temporal binomial regression model was applied to serological test results collected from veterinarians throughout the contiguous USA between January 2013 and November 2019. The model was used to quantify both regional and local temporal trends of canine Ehrlichia spp. seroprevalence and identify areas that experienced significant increases in seroprevalence.ResultsRegionally, increasing seroprevalence occurred within several states throughout the central and southeastern states, including Missouri, Arkansas, Mississippi, Alabama, Virginia, North Carolina, Georgia and Texas. The underlying local trends revealed increasing seroprevalence at a finer scale. Clusters of locally increasing seroprevalence were seen from the western Appalachian region into the southern Midwest, along the Atlantic coast in New England, parts of Florida, Illinois, Wisconsin and Minnesota, and in a couple areas of the Mountain region. Clusters of locally decreasing seroprevalence were seen throughout the USA including New York and the mid-Atlantic states, Texas, the Midwest, and California.ConclusionsCanine Ehrlichia spp. seroprevalence is increasing in both endemic and non-endemic areas of the USA. The findings from this study indicate that dogs across a wide area of the USA are at risk of exposure and these results should provide veterinarians and pet owners with the information they need to make informed decisions about prevention of tick exposure.

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury.

BackgroundMore than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients’ data to extract the temporal features using their structural temporal patterns, that is, trends.ObjectiveThis study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI).MethodsUsing the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation.ResultsRandom forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001).ConclusionsClassifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

Regional and local temporal trends in the prevalence of canine heartworm infection in the contiguous United States: 2012\u20132018

BackgroundCanine heartworm disease is a potentially fatal disease for which treatment is financially burdensome for many pet owners. Prevention is strongly advocated by the veterinary community along with routine testing for infection during annual wellness examinations. Despite the availability of efficacious chemoprophylaxis, recent reports have suggested that the incidence of heartworm disease in domestic dogs is increasing.ResultsUsing data from tests for heartworm infection in the USA from January 2012 through September 2018, a Bayesian spatio-temporal binomial regression model was used to estimate the regional and local temporal trends of heartworm infection prevalence. The area with the largest increase in regional prevalence was found in the Lower Mississippi River Valley. Regional prevalence increased throughout the southeastern states and northward into Illinois and Indiana. Local (county-level) prevalence varied across the USA, with increasing prevalence occurring along most of the Atlantic coast, central United States, and western states. Clusters of decreasing prevalence were present along the Mississippi Alluvial Plain (a historically endemic area), Oklahoma and Kansas, and Florida.ConclusionsCanine heartworm infection prevalence is increasing in much of the USA, both regionally and locally, despite veterinarian recommendations on prevention and testing. Additional steps should be taken to protect dogs, cats and ferrets. Further work is needed to identify the driving factors of the locally decreasing prevalence present along the Mississippi Alluvial plain, Florida, and other areas.

A Spatial View of How United States Cesarean Section Rates Changed from 1990 to 2014

The overuse of cesarean sections (C-sections) in the United States is a contested issue. The rate of C-section births in 2015 at 32 percent was over double the World Health Organization recommendation of 10 to 15 percent. We employed spatial statistical methods and data visualization techniques to assess the temporal and spatial trends in C-section rates by county across the United States. Although the national rate of C-section remained stable at the beginning and end of this study period, an increase in rates from 1997 to 2009 was reflected simultaneously in national, state, and individual county rates. Local indicators of spatial dependence did not show spatial clustering as being connected to, or driving, the change, yet the visualization methods used here show details on individual county deviance from local temporal trends. By highlighting counties that do not follow the trends of their neighbors, we identify exceptional locations that could help further the study of the determinants of changing C-section rates in the United States. Key Words: cesarean sections, exploratory spatial data analysis, medical geography, spatial statistics.

Interrupted time series analysis of children's blood lead levels: A case study of lead hazard control program in Syracuse, New York.

Children’s blood lead concentrations have been closely monitored over the last two decades in the United States. The bio-monitoring surveillance data collected in local agencies reflected the local temporal trends of children’s blood lead levels (BLLs). However, the analysis and modeling of the long-term time series of BLLs have rarely been reported. We attempted to quantify the long-term trends of children’s BLLs in the city of Syracuse, New York and evaluate the impacts of local lead poisoning prevention programs and Lead Hazard Control Program on reducing the children’s BLLs. We applied interrupted time series analysis on the monthly time series of BLLs surveillance data and used ARMA (autoregressive and moving average) models to measure the average children’s blood lead level shift and detect the seasonal pattern change. Our results showed that there were three intervention stages over the past 20 years to reduce children’s BLLs in the city of Syracuse, NY. The average of children’s BLLs was significantly decreased after the interventions, declining from 8.77μg/dL to 3.94μg/dL during1992 to 2011. The seasonal variation diminished over the past decade, but more short term influences were in the variation. The lead hazard control treatment intervention proved effective in reducing the children’s blood lead levels in Syracuse, NY. Also, the reduction of the seasonal variation of children’s BLLs reflected the impacts of the local lead-based paint mitigation program. The replacement of window and door was the major cost of lead house abatement. However, soil lead was not considered a major source of lead hazard in our analysis.

Can protected areas mitigate the impacts of climate change on bird's species and communities?

AbstractAimProtected areas (PAs) are the mainstay of our conservation strategies. While they may succeed in locally preventing species and habitat degradation due to human activities, their ability to mitigate the impacts of climate change on biodiversity is still debated. We assessed whether community and species responses to climate change were related to PAs by testing three main predictions: (1) the thermal adjustment of community composition to temperature changes should be positively related to the proportion of PAs, (2) the species that benefit most from PAs should be less impacted by temperature change, and (3) the species a priori considered the most vulnerable to global change should be even more sensitive to the mitigating effect of PAs.LocationMainland France.MethodsData from a long‐term, large‐scale standardized monitoring programme, recording annual changes in the abundance of 116 breeding bird species in France between 2001 and 2012, were used. Local temporal trends in spring temperature, community reshuffling and bird populations over the country were estimated with a moving window approach (2094 spatial windows). Generalized additive mixed models were then performed to relate these responses to the local proportion of PAs.ResultsMost PAs promote community adjustment to temperature changes. At the species scale, our results show that the more a species benefited from PAs, the less vulnerable it was to temperature changes. PAs were also more effective in mitigating the impact of climate change on the less common and northernmost birds.Main conclusionProtected areas do seem to mitigate climate change impacts on species and communities. Our study argues for the use of integrative frameworks at different biological scales to assess the usefulness and relevance of PAs faced with climate change and suggests that PAs remain key effective conservation strategies in a changing climatic world.