scholarly journals Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Julia Ledien ◽  
Zulma M. Cucunubá ◽  
Gabriel Parra-Henao ◽  
Eliana Rodríguez-Monguí ◽  
Andrew P. Dobson ◽  
...  

AbstractAge-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

2021 ◽  
Author(s):  
Sansiddh Jain ◽  
Avtansh Tiwari ◽  
Nayana Bannur ◽  
Ayush Deva ◽  
Siddhant Shingi ◽  
...  

Forecasting infection case counts and estimating accurate epidemiological parameters are critical components of managing the response to a pandemic. This paper describes a modular, extensible framework for a COVID-19 forecasting system, primarily deployed in Mumbai and Jharkhand, India. We employ a variant of the SEIR compartmental model motivated by the nature of the available data and operational constraints. We estimate best-fit parameters using sequential Model-Based Optimization (SMBO) and describe the use of a novel, fast, and approximate Bayesian model averaging method (ABMA) for parameter uncertainty estimation that compares well with a more rigorous Markov Chain Monte Carlo (MCMC) approach in practice. We address on-the-ground deployment challenges such as spikes in the reported input data using a novel weighted smooth-ing method. We describe extensive empirical analyses to evaluate the accuracy of our method on ground truth as well as against other state-of-the-art approaches. Finally, we outline deployment lessons and describe how inferred model parameters were used by government partners to interpret the state of the epidemic and how model forecasts were used to estimate staffing and planning needs essential for addressing COVID-19 hospital burden.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Siqi Xu ◽  
Yifeng Zhang ◽  
Xiaodan Chen

Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.


Oecologia ◽  
2011 ◽  
Vol 168 (3) ◽  
pp. 719-726 ◽  
Author(s):  
Véronique St-Louis ◽  
Murray K. Clayton ◽  
Anna M. Pidgeon ◽  
Volker C. Radeloff

Plant Disease ◽  
2008 ◽  
Vol 92 (10) ◽  
pp. 1394-1399 ◽  
Author(s):  
Warren E. Copes ◽  
Katherine L. Stevenson

A pictorial key was developed and the relationship between disease severity (S) and incidence (I) was examined to aid in the assessment of black root rot of pansy caused by Thielaviopsis basicola. The key consisted of photographs of root segments that represented nine disease severity levels ranging from 1 to 91%. Pansies that had received different fertility treatments, as part of seven separate experiments, were inoculated with T. basicola. Four weeks after inoculation, roots were washed, and incidence and severity of black root rot were visually assessed using a grid-line-intersect method. Disease incidence ranged from 1.3 to 100%, and severity ranged from 0.1 to 21.4% per plant. Four different mathematical models were compared to quantitatively describe the I-S relationship for the combined data from all seven experiments. Although all models provided an adequate fit, the model that is analogous to the Kono-Sugino equation provided the most reliable estimate of severity over the entire range of disease incidence values. The predictive ability and accuracy of this model across data sets was verified by jackknife and cross-validation techniques. We concluded that incidence of black root rot in pansy can be assessed more objectively and with greater precision than disease severity and can be used to provide reliable estimates of disease severity based on derived regression equations that quantify the I-S relationship for black root rot.


ISRN Ecology ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Diane M. Styers ◽  
Arthur H. Chappelka ◽  
Greg L. Somers

Our overall goal was to examine forest condition across different land use types through measurement of various biotic, abiotic, and anthropogenic variables. Thirty-six permanent 0.05-ha circular plots were established along an urban-rural gradient near Columbus, Ga, USA. In general, forest structure did not differ by land use type for the majority of variables measured. However, urban forests contained less total tree and hardwood species than developing or rural areas. Regarding forest condition, no differences were observed for pest or disease incidence by land use, but more mechanical injury (broken branches, wounds, etc.) was found in urban locales. Lichens were the most sensitive indicator of possible changes in forest condition. Lichen incidence, abundance, and species richness were the greatest in rural forests and the least in urban locations. These factors were related to several indicators of urbanization such as housing density and distance from roads. In this case study subtle, but significant changes in forest structure and condition may have resulted from alterations in land use patterns.


2016 ◽  
Author(s):  
Parichoy Pal Choudhury ◽  
Paige Maas ◽  
Amber Wilcox ◽  
William Wheeler ◽  
Mark Brook ◽  
...  

AbstractThis report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual’s risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.


Parasitology ◽  
2020 ◽  
Vol 147 (13) ◽  
pp. 1552-1558
Author(s):  
G. J. B. Sousa ◽  
M. S. Farias ◽  
V. R. F. Cestari ◽  
T. S. Garces ◽  
T. A. Maranhão ◽  
...  

AbstractChagas disease (CD) is a neglected disease and endemic in Brazil. In the Brazilian Northeast Region, it affects millions of people. Therefore, it is necessary to identify the spatiotemporal trends of CD mortality in the Northeast of Brazil. This ecological study was designed, in which the unit of analysis was the municipality of the Brazilian northeast. The data source was the Information System of Mortality. It was calculated relative risk from socioeconomic characteristics. Mortality rates were smoothed by the Local Empirical Bayes method. Spatial dependency was analysed by the Global and Local Moran Index. Scan spatial statistics were also used. A total of 11 287 deaths by CD were notified in the study. An expressive parcel of this number was observed among 70-year-olds or more (n = 4381; 38.8%), no schooling (n = 4381; 38.8%), mixed-race (n = 4381; 62.3%), male (n = 6875; 60.9%). It was observed positive spatial autocorrelation, mostly in municipalities of the state of Bahia, Piauí (with high-high clusters), and Maranhão (with low-low clusters). The spatial scan statistics has presented a risk of mortality in 24 purely spatial clusters (P < 0.05). The study has identified the spatial pattern of CD mortality mostly in Bahia and Piauí, highlighting priority areas in planning and control strategies of the health services.


2020 ◽  
Vol 18 (3) ◽  
pp. e0405
Author(s):  
Yousef Naderi ◽  
Saadat Sadeghi

Aim of study: To predict genomic accuracy of binary traits considering different rates of disease incidence.Area of study: SimulationMaterial and methods: Two machine learning algorithms including Boosting and Random Forest (RF) as well as threshold BayesA (TBA) and genomic BLUP (GBLUP) were employed. The predictive ability methods were evaluated for different genomic architectures using imputed (i.e. 2.5K, 12.5K and 25K panels) and their original 50K genotypes. We evaluated the three strategies with different rates of disease incidence (including 16%, 50% and 84% threshold points) and their effects on genomic prediction accuracy.Main results: Genotype imputation performed poorly to estimate the predictive ability of GBLUP, RF, Boosting and TBA methods when using the low-density single nucleotide polymorphisms (SNPs) chip in low linkage disequilibrium (LD) scenarios. The highest predictive ability, when the rate of disease incidence into the training set was 16%, belonged to GBLUP, RF, Boosting and TBA methods. Across different genomic architectures, the Boosting method performed better than TBA, GBLUP and RF methods for all scenarios and proportions of the marker sets imputed. Regarding the changes, the RF resulted in a further reduction compared to Boosting, TBA and GBLUP, especially when the applied data set contained 2.5K panels of the imputed genotypes.Research highlights: Generally, considering high sensitivity of methods to imputation errors, the application of imputed genotypes using RF method should be carefully evaluated.


Author(s):  
Shamsul J Elias ◽  
M.N.M Warip ◽  
M. Elshaikh ◽  
M. Yusof Darus ◽  
R Badlishah Ahmad

<span>Vehicle density and high vehicle mobility are variables that measured the performance of Vehicular Ad Hoc Network (VANET) in unpredictable traffic data transmission environment. This paper is focused on non-safety messages transmission mainly for delay in time in test-bed simulation environment. Network optimization is an approach to evaluate the existing congestion control protocols and other network parameters for outlining a newly enhanced congestion control protocols. This paper presents a city and highway traffic data transmission scenarios for optimizing delay sensitivity utilizing the Taguchi method.  The avareage data transmission on delay is performance indicator applying OMNeT++ simulation tools. The optimization process could be achieved once the best fit performance parameters are being identified. The best fit performance values could conclude the optimal and efficient congestion control networks. The packet sizes are the main control factors for this test-bed experiment focusing on non-safety messages which are delay sensitive.</span>


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