scholarly journals The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

2020 ◽  
Vol 44 (5) ◽  
pp. 591-604 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Ángel Serrano-Aroca
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Transmission Risk ◽  
Temporal Models ◽  
Wide Range ◽  
The World ◽  
Modelling Techniques ◽  
Spatio Temporal ◽  
Hot Weather

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

scholarly journals Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

2020 ◽  
Vol 10 (8) ◽  
pp. 3091-3110 ◽  
Author(s):  
M. E. Wigwe ◽  
E. S. Bougre ◽  
M. C. Watson ◽  
A. Giussani
Keyword(s):  
Oil And Gas ◽  
Generalized Additive Models ◽  
Gas Production ◽  
Additive Models ◽  
Machine Learning Algorithms ◽  
Production Performance ◽  
Spatial And Temporal Variability ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Production Prediction

Abstract Modern data analytic techniques, statistical and machine-learning algorithms have received widespread applications for solving oil and gas problems. As we face problems of parent–child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We accomplish this using a well-formulated spatio-temporal (ST) model. In this paper, we present a multi-basin study of production performance evaluation and applications of ST models for oil and gas data. We analyzed dataset from 10,077 horizontal wells from 2008 to 2019 in five unconventional formations in the USA: Bakken, Marcellus, Eagleford, Wolfcamp, and Bone Spring formations. We evaluated well production performance and performance of new completions over time. Results show increased productivity of oil and gas since 2008. Also, the Bakken wells performed better for the counties evaluated. We present two methods for fitting spatio-temporal models: fixed rank kriging and ST generalized additive models using thin plate and cubic regression splines as basis functions in the spline-based smooths. Results show a significant effect on production by the smooth term, accounting for between 60 and 95% of the variability in the six-month production. Overall, we saw a better production response to completions for the gas formations compared to oil-rich plays. The results highlight the benefits of spatio-temporal models in production prediction as it implicitly accounts for geology and technological changes with time.

scholarly journals Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers' Cohort

2021 ◽  
Author(s):  
Cervantes - Martínez Karla ◽  
Riojas - Rodríguez Horacio ◽  
Díaz - Ávalos Carlos ◽  
Moreno - Macías Hortensia ◽  
López - Ridaura Ruy ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Generalized Additive Models ◽  
Epidemiological Studies ◽  
Additive Models ◽  
Temporal Modeling ◽  
Secondary Sources ◽  
Temporal Models ◽  
Out Of Sample ◽  
Spatio Temporal

Abstract Epidemiological studies on the effects of air pollution in Mexico often use the environmental concentrations of monitors closest to the home as exposure proxies, yet this approach disregards the space gradients of pollutants and assumes that individuals have no intra-city mobility. Our aim was to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in a population of ~ 16 500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants. Using information from secondary sources on geographic and meteorological variables as well as other pollutants, we fitted two generalized additive models to predict monthly PM2.5 and NO2 concentrations in the 2004–2019 period. The models were evaluated through 10-fold cross validation. Both showed high predictive accuracy with out-of-sample data and no overfitting (CV RMSE = 0.102 for PM2.5 and CV RMSE = 4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) µg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a solid alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Valle de México region.

scholarly journals Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Melkamu Dedefo ◽  
Henry Mwambi ◽  
Sileshi Fanta ◽  
Nega Assefa
Keyword(s):  
Cause Of Death ◽  
Linear Models ◽  
Temporal Distribution ◽  
Mortality Data ◽  
Contributing Factors ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Non Parametric

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

scholarly journals Geocoding and spatio-temporal modeling of long-term PM2.5 and NO2 exposure: the Mexican Teachers´ Cohort

2021 ◽  
Author(s):  
KARLA CERVANTES-MARTINEZ ◽  
HORACIO RIOJAS-RODRÍGUEZ ◽  
CARLOS DÍAZ-AVALOS ◽  
HORTENSIA MORENO-MACÍAS ◽  
RUY LÓPEZ-RIDAURA ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Human Mobility ◽  
Epidemiological Studies ◽  
Additive Models ◽  
Secondary Source ◽  
Source Information ◽  
Promising Alternative ◽  
Temporal Models ◽  
Spatio Temporal

Epidemiological studies on air pollution in Mexico often use the environmental concentrations of pollutants as measured by monitors closest to the home of participants as exposure proxies, yet this approach does not account for the space gradients of pollutants and ignores intra-city human mobility. This study aimed to develop high-resolution spatial and temporal models for predicting long-term exposure to PM2.5 and NO2 in ~16,500 participants from the Mexican Teachers’ Cohort study. We geocoded the home and work addresses of participants, and used secondary source information on geographical and meteorological variables as well as other pollutants to fit two generalized additive models capable of predicting monthly PM2.5 and NO2 concentrations during the 2004-2019 period. Both models were evaluated through 10-fold cross-validation, and showed high predictive accuracy with out-of-sample data and no overfitting (CV-RMSE=0.102 for PM2.5 and CV-RMSE=4.497 for NO2). Participants were exposed to a monthly average of 24.38 (6.78) mg/m3 of PM2.5 and 28.21 (8.00) ppb of NO2 during the study period. These models offer a promising alternative for estimating PM2.5 and NO2 exposure with high spatio-temporal resolution for epidemiological studies in the Mexico City Metropolitan Area.

scholarly journals The Evolution of Data Sharing Practices in the Psychological Literature

2021 ◽  
Author(s):  
Judith Neve ◽  
Guillaume A Rousselet
Keyword(s):  
Data Sharing ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Future Research ◽  
Science Data ◽  
Editorial Policies ◽  
Psychological Science ◽  
The Impact

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We examined the effect of these policies over a longer term by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. We fit generalized additive models as these allow for a flexible assessment of the effect of time, in particular to identify non-linear changes in the trend. These models were compared to generalized linear models to examine whether the non-linearity is needed. Descriptive results and the outputs from generalized additive and linear models were coherent with previous findings: following the policies in Cognition and Psychological Science, data sharing statement rates increased immediately and continued to increase beyond the timeframes examined previously, until reaching close to 100%. In Clinical Psychological Science, data sharing statement rates started to increase only two years following the implementation of badges. Reusability rates jumped from close to 0% to around 50% but did not show changes within the pre-policy nor the post-policy timeframes. Journals that did not implement a policy showed no change in data sharing rates or reusability over time. There was variability across journals in the levels of increase, so we suggest future research should examine a larger number of policies to draw conclusions about their efficacy. We also encourage future research to investigate the barriers to data sharing specific to psychology subfields to identify the best interventions to tackle them.

scholarly journals An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets

2011 ◽  
Vol 68 (10) ◽  
pp. 2252-2263 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Youen Vermard ◽  
Trevor Hutton ◽  
Ane Iriondo ◽  
Angélique Jadaud ◽  
...  
Keyword(s):  
Linear Models ◽  
Generalized Additive Models ◽  
Technical Information ◽  
Fishing Effort ◽  
Additive Models ◽  
Induced Variation ◽  
Fishing Fleets ◽  
The Impact ◽  
Selection Of ◽  
Experience Effect

Abstract Mahévas, S., Vermard, Y., Hutton, T., Iriondo, A., Jadaud, A., Maravelias, C. D., Punzón, A., Sacchi, J., Tidd, A., Tsitsika, E., Marchal, P., Goascoz, N., Mortreux, S., and Roos, D. 2011. An investigation of human vs. technology-induced variation in catchability for a selection of European fishing fleets. – ICES Journal of Marine Science, 68: 2252–2263. The impact of the fishing effort exerted by a vessel on a population depends on catchability, which depends on population accessibility and fishing power. The work investigated whether the variation in fishing power could be the result of the technical characteristics of a vessel and/or its gear or whether it is a reflection of inter-vessel differences not accounted for by the technical attributes. These inter-vessel differences could be indicative of a skipper/crew experience effect. To improve understanding of the relationships, landings per unit effort (lpue) from logbooks and technical information on vessels and gears (collected during interviews) were used to identify variables that explained variations in fishing power. The analysis was undertaken by applying a combination of generalized additive models and generalized linear models to data from several European fleets. The study highlights the fact that taking into account information that is not routinely collected, e.g. length of headline, weight of otter boards, or type of groundrope, will significantly improve the modelled relationships between lpue and the variables that measure relative fishing power. The magnitude of the skipper/crew experience effect was weaker than the technical effect of the vessel and/or its gear.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

scholarly journals Percolation models of pathogen spillover

2019 ◽  
Vol 374 (1782) ◽  
pp. 20180331 ◽  
Author(s):  
Alex D. Washburne ◽  
Daniel E. Crowley ◽  
Daniel J. Becker ◽  
Kezia R. Manlove ◽  
Marissa L. Childs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Mechanistic Model ◽  
Piecewise Linear ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Management Intervention ◽  
Management Concepts ◽  
The Impact ◽  
Reservoir Hosts

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

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