Statistics For Spatial Data

The catastrophic outbreak of Severe Acute Respiratory Syndrome - Coronavirus (SARS-CoV-2) also known as COVID-2019 has brought the worldwide threat to the living society. The whole world is putting incredible efforts to fight against the spread of this deadly disease in terms of infrastructure, finance, data sources, protective gears, life-risk treatments and several other resources. The artificial intelligence researchers are focusing their expertise knowledge to develop mathematical models for analyzing this epidemic situation using nationwide shared data. To contribute towards the well-being of living society, this article proposes to utilize the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins dashboard.

/pdf/covid-19-epidemic-analysis-using-machine-learning-and-deep-1nqmh9ex85.pdf

COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms

COVID-19 data from India is compared against several countries as well as key states in the US with a major outbreak, and it is found that the basic reproduction number R0 for India is in the expected range of 1.4-3.9. Further, the rate of growth of infections in India is very close to that in Washington and California. Exponential and classic susceptible-infected-recovered (SIR) models based on available data are used to make short and long-term predictions on a daily basis. Based on the SIR model, it is estimated that India will enter equilibrium by the end of May 2020 with the final epidemic size of approximately 13,000. However, this estimation will be invalid if India enters the stage of community transmission. The impact of social distancing, again with the assumption of no community transmission, is also assessed by comparing data from different geographical locations.

/pdf/predictions-for-covid-19-outbreak-in-india-using-2yy25hvjx9.pdf

Predictions for COVID-19 outbreak in India using Epidemiological models

Importance: India has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3, soon after the analysis of this paper was completed.

Objective: To study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic.

Design, Setting, and Participants: We use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia. 

Main Outcome and Measures: The current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI).

Results: Our predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in people9s adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March. 

Conclusions and Relevance: The lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies.

Software: Our contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.

/pdf/predictions-role-of-interventions-and-effects-of-a-historic-425pm608qv.pdf

Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms

[1] We propose a method of analyzing spatiotemporal data by decomposition into deterministic nonparametric functions of time and space, linear functions of other covariates, and a random component that is spatially, though not temporally, correlated. The resulting model is used for spatial interpolation and especially for estimation of a spatially dependent temporal average. The results are applied to part of the PM 2.5 network established by the U.S. Environmental Protection Agency, covering three southeastern U.S. states. A novel feature of the analysis is a variant of the expectation-maximization algorithm to account for missing data. The results show, among other things, that a substantial part of the region is in violation of the proposed long-term average standard for PM 2.5 .

Spatiotemporal modeling of PM2.5 data with missing values : Application of recent advances in space-time statistics to atmospheric data

The ongoing pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge threat to global public health, it is of utmost importance to identify the rate at which the disease is spreading. In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID-19 outbreak. We also incorporate information on total or partial lockdown, wherever available, into the model. The model is concise in structure, and using appropriate diagnostic measures, we showed that a time-dependent quadratic trend successfully captures the incidence pattern of the disease. We also estimate the basic reproduction number across different countries, and find that it is consistent except for the United States of America. The above statistical analysis is able to shed light on understanding the trends of the outbreak, and gives insight on what epidemiological stage a region is in. This has the potential to help in prompting policies to address COVID-19 pandemic in different countries.

A time series method to analyze incidence pattern and estimate reproduction number of COVID-19

BACKGROUND: Firearm-related fatalities are a top 3 cause of death among children in the United States. Despite historical declines in firearm ownership, the firearm-related mortality rate among young children has risen over the past decade. In this study, we examined changes in firearm ownership among families with young children from 1976 to 2016, exploring how such changes relate to recent increases in firearm-related mortality among 1- to 5-year-olds. METHODS: Individual-level data from the National Vital Statistics System were merged with household-level data from the General Social Survey to create national-level estimates of firearm-related child mortality and family firearm ownership from 1976 to 2016 (n = 41 years). Vector autoregression models were used to examine the association between firearm ownership and child mortality. RESULTS: The proportion of non-Hispanic white families with young children who owned firearms declined from 50% in 1976 to 45% in 2016 and from 38% to 6% among non-Hispanic African American families. The proportion of white families with young children who owned handguns, however, increased from 25% to 32%; 72% of firearm-owning families with young children now own a handgun. Increases in handgun ownership partially explained the recent rise in firearm-related white child mortality (B = 0.426), net of economic conditions, and sociodemographic characteristics of firearm-owning families. CONCLUSIONS: Changes in the types of firearms in the homes of US families may partially explain recently rising firearm-related mortality among young white children. These findings hold relevance for pediatricians and policy makers aiming to reduce firearm-related mortality and promote firearm safety in children’s homes.

Family Firearm Ownership and Firearm-Related Mortality Among Young Children: 1976-2016.

Recent Coronavirus pandemic has prompted many regulations which are affecting the stock market Especially because of lockdown policies across the world, the airlines industry is suffering We analyse the stock price movements of three major airlines companies using a new approach which leverages a measure of internet concern on different topics In this approach, Twitter data and Google Trends are used to create a set of predictors which then leads to an appropriately modified GARCH model In the analysis, first we show that the ongoing pandemic has an unprecedented severe effect Then, the proposed model is used to analyse and forecast stock price volatility of the airlines companies The findings establish that our approach can successfully use the effects of internet concern for different topics on the movement of stock price index and provide good forecasting accuracy Model confidence set (MCS) procedure further shows that the short-term volatility forecasts are more accurate for this method than other candidate models Thus, it can be used to understand the stock market during a pandemic in a better way Further, the proposed approach is attractive and flexible, and can be extended to other related problems as well

Analyzing airlines stock price volatility during COVID-19 pandemic through internet search data

For a general class of stationary random fields we study asymptotic properties of the discrete Fourier transform (DFT), periodogram, parametric and nonparametric spectral density estimators under an easily verifiable short-range dependence condition expressed in terms of functional dependence measures. We allow irregularly spaced data which is indexed by a subset $\Gamma $ of $\mathbb{Z}^{d}$. Our asymptotic theory requires minimal restriction on the index set $\Gamma $. Asymptotic normality is derived for kernel spectral density estimators and the Whittle estimator of a parameterized spectral density function. We also develop asymptotic results for a covariance matrix estimate.

/pdf/an-asymptotic-theory-for-spectral-analysis-of-random-fields-vnyy4k8v3g.pdf

An asymptotic theory for spectral analysis of random fields

Stochastic simulation algorithms provide a powerful means to understand complex biochemical processes as well as to solve the inverse problem of reconstructing hidden states and parameters from experimental single-cell data. At presence, a repertoire of efficient algorithms for simulating and calibrating stochastic reaction networks is available. However, most of these approaches do not account for the fact that each cell of a clonal population is exposed to a random extrinsic environment, i.e., the agglomerate of so-called extrinsic factors such as cell size, shape or cell cycle stage. We recently proposed a dynamic description of stochastic chemical kinetics in random but unknown extrinsic environments, reflected by a stochastic process where uncertain parameters are marginalized out. In this work we further investigate that process and provide additional analytical results. We demonstrate the marginalization using several biologically relevant parameter distributions and derive exact waiting-time distributions. We further show that the marginalized process model can achieve a variance reduction in the context of parameter inference.

/pdf/marginal-dynamics-of-stochastic-biochemical-networks-in-58yj70d945.pdf

Soudeep Deb

Papers

A time series method to analyze incidence pattern and estimate reproduction number of COVID-19

Family Firearm Ownership and Firearm-Related Mortality Among Young Children: 1976-2016.

Analyzing airlines stock price volatility during COVID-19 pandemic through internet search data

An asymptotic theory for spectral analysis of random fields

Marginal dynamics of stochastic biochemical networks in random environments