scholarly journals An international assessment of the COVID-19 pandemic using ensemble data assimilation

2020 ◽  
Author(s):  
Geir Evensen ◽  
Javier Amezcua ◽  
Marc Bocquet ◽  
Alberto Carrassi ◽  
Alban Farchi ◽  
...  
Keyword(s):  
Model System ◽  
Reproductive Number ◽  
Model Parameters ◽  
Data Sets ◽  
Intervention Measures ◽  
Uncertainty Estimates ◽  
Complex Models ◽  
Epidemic Development ◽  
Iterative Ensemble Smoothers ◽  
The Impact

AbstractThis work shows how one can use iterative ensemble smoothers to effectively estimate parameters of an SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters leading to model predictions in close agreement with the observations. The updated ensemble of model simulations have predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development given knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries with vastly different developments of the epidemic, and we can accurately model the development of the COVID-19 outbreak in these countries. We realize that more complex models, e.g., with regional compartments, may be desirable, and we suggest that the approach used here should be applicable also for these models.

The sensitivity of sea ice growth to the choice of ice shelf-ocean coupling algorithms.

2020 ◽  
Author(s):  
Stefan Jendersie ◽  
Alena Malyarenko
Keyword(s):  
Sea Ice ◽  
Ocean Model ◽  
Data Sets ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Ice Growth ◽  
Continental Scale ◽  
Complex Models ◽  
Coupling Algorithms ◽  
The Impact

<p>To quantify Antarctic ice mass loss and the subsequent sea level rise the geophysical modelling community is pushing towards frameworks that fully couple increasingly complex models of atmosphere, ocean, sea ice and ice sheets & shelves.  One particular hurdle remains the accurate representation of the vertical ocean-ice interaction at the base of ice shelves.  Parameterizations that are tuned to particular data sets naturally perform best in comparable ice shelf cavity environments. This poses the challenge in continental scale ocean-ice shelf models to chose one melt parameterizaton that performs sufficiently well in diverse cavity environment.  Thus adding uncertainty in ice shelf induced ocean freshening crucially affects modelled sea ice growth.  The impact magnitude of ice shelf supplied melt water on growth rates, thickness and extent of sea ice in the open ocean is currently debated in the literature.  <br>We reviewed and compared 16 commonly utilized melting/freezing parameterizations in coupled ocean-ice shelf models.  Melt rates differ hugely, in identical idealized conditions between 0.1m/yr to 3m/yr.  In this talk we present results of a realistic circum-Antarctic ice shelf and sea ice coupled ocean model (CICE, ROMS), where we look at the effects of the chosen ice shelf melt parameterization on modeled sea surface conditions and sea ice growth, regionally and circum Antarctic.</p>

scholarly journals Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa

2021 ◽  
Author(s):  
Mark Wamalwa ◽  
Henri E.Z. Tonnang
Keyword(s):  
Food Systems ◽  
Negative Impact ◽  
South Sudan ◽  
Eastern Africa ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Mortality Data ◽  
African Countries ◽  
Intervention Measures ◽  
The Impact

Abstract BackgroundThe emergence of Coronavirus disease 2019 (COVID-19) as a global pandemic presents a serious health threat to African countries and the livelihoods of its people. To mitigate the impact of this disease, these countries implemented intervention measures including self-isolation, the closure of schools, banning of public gatherings, social distancing and border closures. Several epidemiological models have been used to improve our understanding of COVID-19 trajectory. This has helped inform decisions about pandemic planning, resource allocation, implementation of other non-pharmaceutical interventions (NPIs). This study presents estimates of the cases and fatalities due to COVID-19 and attempts to forecast the impact of governmental interventions in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda. .MethodsWe used time series COVID-19 case and mortality data collated from the Johns Hopkins University (JHU) repository and an extended susceptible-infected-removed (eSIR) compartmental model incorporating quarantine and vaccination compartments to account for transmission dynamics and vaccine-induced immunity over time. The predication accuracy was evaluated using the root mean square error and mean absolute error.ResultsThe number of new and confirmed cases show an exponential trend since March 02 2020. The mean basic reproductive number (R0) was between 1.32 (95% CI, 1.17 - 1.49) in Rwanda and 8.52 (95% CI: 3.73 - 14.10) in Kenya, under exponential growth. There would be a total of 115,505 (95% CI:109,999 - 121,264), 7,072,584 (6,945,505 - 7,203,084), 18,248,566(18,100,299 - 18,391,438), 410,599 (399,776 - 421528), 386,020 (376,478 - 396244), 107,265 (95,757 - 119982), 3,145,602 (3,089,070 - 3205017) infected cases under the current country blockade by January 16/2022 in Burundi, Ethiopia, Kenya, Rwanda, South Sudan, Tanzania and Uganda respectively. We show that the low apparent morbidity and mortality observed in EACs, is likely biased by underestimation of infected and mortality cases.ConclusionThe current NPI measures can effectively reduce further spread of COVID-19 and should be strengthened. The observed reduction in R0 is consistent with intervention measures implemented in EACs, in particular, lockdowns and roll-out of vaccination programmes. Future work should account for the negative impact of the interventions to the economy and food systems.

scholarly journals Using the leave-two-out method to determine the optimal statistical crop model

2021 ◽  
Author(s):  
Thi Lan Anh Dinh ◽  
Filipe Aires
Keyword(s):  
Crop Yield ◽  
Cross Validation ◽  
Model Complexity ◽  
Training Dataset ◽  
Validation Dataset ◽  
Model Parameters ◽  
Testing Dataset ◽  
Complex Models ◽  
Leave One Out ◽  
The Impact

Abstract. The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterised by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimise the model parameters, the validation datasets to select the best model, and the testing dataset to evaluate the model generalisation ability. Splitting the overall database into three datasets is impossible in crop yield modelling. The leave-one-out cross-validation method or simply leave-one-out (LOO) has been introduced to facilitate statistical modelling when the database is limited. However, the model choice is made using the testing dataset, which can be misleading by favouring unnecessarily complex models. The nested cross-validation approach was introduced in machine learning to avoid this problem by truly utilising three datasets, especially problems with limited databases. In this study, we proposed one particular implementation of the nested cross-validation, called the leave-two-out method (LTO), to chose the best model with an optimal model complexity (using the validation dataset) and estimated the true model quality (using the testing dataset). Two applications are considered: Robusta coffee in Cu M'gar (Dak Lak, Vietnam) and grain maize over 96 French departments. In both cases, LOO is misleading by choosing too complex models; LTO indicates that simpler models actually perform better when a reliable generalisation test is considered. The simple models obtained using the LTO approach have reasonable yield anomaly forecasting skills in both study crops. This LTO approach can also be used in seasonal forecasting applications. We suggest that the LTO method should become a standard procedure for statistical crop modelling.

scholarly journals Analysis of COVID-19 Prevention and Control Effects Based on the SEITRD Dynamic Model and Wuhan Epidemic Statistics

2020 ◽  
Vol 17 (24) ◽  
pp. 9309
Author(s):  
Yusheng Zhang ◽  
Liang Li ◽  
Yuewen Jiang ◽  
Biqing Huang
Keyword(s):  
Dynamic Model ◽  
Control Method ◽  
Control Strategies ◽  
Dynamical Behavior ◽  
Model Parameters ◽  
Time Control ◽  
Intervention Measures ◽  
Travel Restrictions ◽  
And Control ◽  
The Impact

Since December 2019, millions of people worldwide have been diagnosed with COVID-19, which has caused enormous losses. Given that there are currently no effective treatment or prevention drugs, most countries and regions mainly rely on quarantine and travel restrictions to prevent the spread of the epidemic. How to find proper prevention and treatment methods has been a hot topic of discussion. The key to the problem is to understand when these intervention measures are the best strategies for disease control and how they might affect disease dynamics. In this paper, we build a transmission dynamic model in combination with the transmission characteristics of COVID-19. We thoroughly study the dynamical behavior of the model and analyze how to determine the relevant parameters, and how the parameters influence the transmission process. Furthermore, we subsequently compare the impact of different control strategies on the epidemic, the variables include intervention time, control duration, control intensity, and other model parameters. Finally, we can find a better control method by comparing the results under different schemes and choose the proper preventive control strategy according to the actual epidemic stage and control objectives.

scholarly journals Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets

2019 ◽  
Vol 52 (3) ◽  
pp. 397-423
Author(s):  
Luc Steinbuch ◽  
Thomas G. Orton ◽  
Dick J. Brus
Keyword(s):  
Prior Distribution ◽  
Crop Yields ◽  
Simulated Data ◽  
Monte Carlo Sampling ◽  
Model Parameters ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Minimal Impact ◽  
The Impact

AbstractArea-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller ($$<\,50 $$<50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is found to have minimal impact on the disaggregated predictions. In most cases with known short-range behaviour, an approach that disregards uncertainty in the variogram distance parameter gives a reasonable assessment of prediction uncertainty. However, some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK.

scholarly journals Effect of data error correlations on trans-dimensional MT Bayesian inversions

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Rongwen Guo ◽  
Liming Liu ◽  
Jianxin Liu ◽  
Ya Sun ◽  
Rong Liu
Keyword(s):  
Correlation Effect ◽  
Monte Carlo Algorithm ◽  
Model Parameters ◽  
Data Errors ◽  
Model Parameterization ◽  
Uncertainty Estimates ◽  
Posterior Probability Density ◽  
Geoelectrical Structures ◽  
Data Error ◽  
The Impact

AbstractReal magnetotelluric (MT) data errors are commonly correlated, but MT inversions routinely neglect such correlations without an investigation on the impact of this simplification. This paper applies a hierarchical trans-dimensional (trans-D) Bayesian inversion to examine the effect of correlated MT data errors on the inversion for subsurface geoelectrical structures, and the model parameterization (the number of conductivity interfaces) is treated as an unknown. In the inversion considering error correlations, the data errors are parameterized by the first-order autoregressive (AR(1)) process, which is included as an unknown in the inversion. The data information itself determines the AR(1) parameter. The trans-D inversion applies the reversible-jump Markov chain Monte Carlo algorithm to sample the trans-D posterior probability density (PPD) for the model parameters, model parameterization and AR(1) parameters, accounting for the uncertainties of the model dimension and data error correlation in the uncertainty estimates of the conductivity profile. In the inversion ignoring the correlation, we neglect the correlation effect by turning off the AR(1) parameter. Then the correlation effect on the MT inversion can be examined upon comparing the posterior marginal conductivity profiles from the two inversions. Further investigation is then carried out for a synthetic case and a real MT data example. The results indicate that for strong correlation cases, neglecting error correlations can significantly affect the inversion results.

scholarly journals Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries

2020 ◽  
Author(s):  
Patrick Bryant ◽  
Arne Elofsson
Keyword(s):  
Behavioural Change ◽  
Science Research ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Mobility Patterns ◽  
School Closing ◽  
Mobility Data ◽  
Epidemic Development ◽  
Direct Feedback ◽  
The Impact

BackgroundAs governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. It is likely different countries and populations respond differently to the same NPIs and that these differences are reflected in the epidemic development.MethodsWe build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R0, due to changes in mobility patterns. We utilize mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R0 is estimated through the model.FindingsThe changes in mobility have a large overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The grocery and pharmacy sector is estimated to account for 97 % of the reduction in R0 (95% confidence interval [0·79,0·99]).InterpretationOur model predicts three-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium.FundingFinancial support: Swedish Research Council for Natural Science, grant No. VR-2016-06301 and Swedish E-science Research Center. Computational resources: Swedish National Infrastructure for Computing, grant No. SNIC-2019/3-319.

scholarly journals Evaluating the burden of COVID-19 on hospital resources in Bahia, Brazil: A modelling-based analysis of 14.8 million individuals

2020 ◽  
Author(s):  
Juliane Fonseca Oliveira ◽  
Daniel C. P. Jorge ◽  
Rafael V. Veiga ◽  
Moreno S. Rodrigues ◽  
Matheus F. Torquato ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Health Resources ◽  
Compartment Model ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Model Parameters ◽  
Resource Setting ◽  
Asymptomatic Individuals ◽  
The One ◽  
The Impact

Here we present a general compartment model with a time-varying transmission rate to describe the dynamics of the COVID-19 epidemic, parameterized with the demographics of Bahia, a state in northeast Brazil. The dynamics of the model are influenced by the number of asymptomatic cases, hospitalization requirements and mortality due to the disease. A locally-informed model was determined using actual hospitalization records. Together with cases and casualty data, optimized estimates for model parameters were obtained within a metaheuristic framework based on Particle Swarm Optimization. Our strategy is supported by a statistical sensitivity analysis on the model parameters, adequate to properly account for the simulated scenarios. First, we evaluated the effect of previously enforced interventions on the transmission rate. Then, we studied its effects on the number of deaths as well as hospitalization requirements, considering the state as a whole. Special attention is given to the impact of asymptomatic individuals on the dynamic of COVID-19 transmission, as these were estimated to contribute to a 68% increase in the basic reproductive number. Finally, we delineated scenarios that can set guides to protect the health care system, particularly by keeping demand below total bed occupancy. Our results underscore the challenges related to maintaining a fully capable health infrastructure during the ongoing COVID-19 pandemic, specially in a low-resource setting such as the one focused in this work. The evidences produced by our modelling-based analysis show that decreasing the transmission rate is paramount to success in maintaining health resources availability, but that current local efforts, leading to a 38% decrease in the transmission rate, are still insufficient to prevent its collapse at peak demand. Carefully planned and timely applied interventions, that result in stark decreases in transmission rate, were found to be the most effective in preventing hospital bed shortages for the longest periods.

scholarly journals The impact of social distancing and epicenter lockdown on the COVID-19 epidemic in mainland China: A data-driven SEIQR model study

2020 ◽  
Author(s):  
Yuzhen Zhang ◽  
Bin Jiang ◽  
Jiamin Yuan ◽  
Yanyun Tao
Keyword(s):  
Mainland China ◽  
Cost Effective ◽  
Data Driven ◽  
Favorable Effect ◽  
Reproductive Number ◽  
Model Parameters ◽  
Susceptible Population ◽  
Social Distancing ◽  
Epidemic Size ◽  
The Impact

AbstractThe outbreak of coronavirus disease 2019 (COVID-19) which originated in Wuhan, China, constitutes a public health emergency of international concern with a very high risk of spread and impact at the global level. We developed data-driven susceptible-exposed-infectious-quarantine-recovered (SEIQR) models to simulate the epidemic with the interventions of social distancing and epicenter lockdown. Population migration data combined with officially reported data were used to estimate model parameters, and then calculated the daily exported infected individuals by estimating the daily infected ratio and daily susceptible population size. As of Jan 01, 2020, the estimated initial number of latently infected individuals was 380.1 (95%-CI: 379.8∼381.0). With 30 days of substantial social distancing, the reproductive number in Wuhan and Hubei was reduced from 2.2 (95%-CI: 1.4∼3.9) to 1.58 (95%-CI: 1.34∼2.07), and in other provinces from 2.56 (95%-CI: 2.43∼2.63) to 1.65 (95%-CI: 1.56∼1.76). We found that earlier intervention of social distancing could significantly limit the epidemic in mainland China. The number of infections could be reduced up to 98.9%, and the number of deaths could be reduced by up to 99.3% as of Feb 23, 2020. However, earlier epicenter lockdown would partially neutralize this favorable effect. Because it would cause in situ deteriorating, which overwhelms the improvement out of the epicenter. To minimize the epidemic size and death, stepwise implementation of social distancing in the epicenter city first, then in the province, and later the whole nation without the epicenter lockdown would be practical and cost-effective.

scholarly journals A COVID-19 model with a nonlinear incidence rate and intervention exit strategies for Kenya

2020 ◽  
Author(s):  
Rachel Waema Mbogo ◽  
Farai Nyabadza
Keyword(s):  
Behavior Change ◽  
Large Scale ◽  
Contact Tracing ◽  
Personal Hygiene ◽  
Model Parameters ◽  
Clear Understanding ◽  
Nonlinear Incidence ◽  
Intervention Measures ◽  
Exit Strategies ◽  
The Impact

Abstract The coronavirus disease (COVID-19) is a novel infection caused by SARS-CoV-2, a corona virus type that has previously not been seen in humans. The speedy spread of COVID-19 globally has greatly affected the socio-economic environments and health systems. To effectively address this rapid spread, it is imperative to have a clear understanding of the COVID-19 transmission dynamics. In this study we evaluate a COVID-19 epidemic model with a nonlinear incidence function and a saturating treatment rate. We propose an SLIRD data driven COVID 19 model which incorporates individual self initiated behavior change of the susceptible individuals. The proposed model allows the evaluation of the impact of easing intervention measures at specific times. To estimate the model parameters, the model was fitted to the daily reported COVID-19 cases in Kenya. Self initiated behavioral responses by individuals and large scale persistent testing proved to be the most effective measures to flatten the epidemic infection curve. Evidence from the simulations points out that, return to normalcy from COVID -19 pandemic will require individual behavior change in adhering to intervention measures and especially proper wearing of face masks and personal hygiene, alongside effective contact-tracing and active testing. The results have significant impact on the management of COVID-19 and implementation of the intervention exit strategies.

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