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Register a new userA Vector Autoregression Prediction Model for COVID-19 Outbreak
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Added by
Qinan Wang
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
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Since two people came down a county of north Seattle with positive COVID-19 (coronavirus-19) in 2019, the current total cases in the United States (U.S.) are over 12 million. Predicting the pandemic trend under effective variables is crucial to help find a way to control the epidemic. Based on available literature, we propose a validated Vector Autoregression (VAR) time series model to predict the positive COVID-19 cases. A real data prediction for U.S. is provided based on the U.S. coronavirus data. The key message from our study is that the situation of the pandemic will getting worse if there is no effective control.
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The coronavirus disease 2019 (COVID-19) had caused more that 8 million infections as of middle June 2020. Recently, Brazil has become a new epicentre of COVID-19, while India and African region are potential epicentres. This study aims to predict the inflection point and outbreak size of these new/potential epicentres at the early phase of the epidemics by borrowing information from more `mature curves from other countries. We modeled the cumulative cases to the well-known sigmoid growth curves to describe the epidemic trends under the mixed-effect models and using the four-parameter logistic model after power transformations. African region is predicted to have the largest total outbreak size of 3.9 million cases (2.2 to 6 million), and the inflection will come around September 13, 2020. Brazil and India are predicted to have a similar final outbreak size of around 2.5 million cases (1.1 to 4.3 million), with the inflection points arriving June 23 and July 26, respectively. We conclude in Brazil, India, and African the epidemics of COVI19 have not yet passed the inflection points; these regions potentially can take over USA in terms of outbreak size
Most COVID-19 predictive modeling efforts use statistical or mathematical models to predict national- and state-level COVID-19 cases or deaths in the future. These approaches assume parameters such as reproduction time, test positivity rate, hospitalization rate, and social intervention effectiveness (masking, distancing, and mobility) are constant. However, the one certainty with the COVID-19 pandemic is that these parameters change over time, as well as vary across counties and states. In fact, the rate of spread over region, hospitalization rate, hospital length of stay and mortality rate, the proportion of the population that is susceptible, test positivity rate, and social behaviors can all change significantly over time. Thus, the quantification of uncertainty becomes critical in making meaningful and accurate forecasts of the future. Bayesian approaches are a natural way to fully represent this uncertainty in mathematical models and have become particularly popular in physics and engineering models. The explicit integration time varying parameters and uncertainty quantification into a hierarchical Bayesian forecast model differentiates the Mayo COVID-19 model from other forecasting models. By accounting for all sources of uncertainty in both parameter estimation as well as future trends with a Bayesian approach, the Mayo COVID-19 model accurately forecasts future cases and hospitalizations, as well as the degree of uncertainty. This approach has been remarkably accurate and a linchpin in Mayo Clinics response to managing the COVID-19 pandemic. The model accurately predicted timing and extent of the summer and fall surges at Mayo Clinic sites, allowing hospital leadership to manage resources effectively to provide a successful pandemic response. This model has also proven to be very useful to the state of Minnesota to help guide difficult policy decisions.
Understanding dynamics of an outbreak like that of COVID-19 is important in designing effective control measures. This study aims to develop an agent based model that compares changes in infection progression by manipulating different parameters in a synthetic population. Model input includes population characteristics like age, sex, working status etc. of each individual and other factors influencing disease dynamics. Depending on number of epicentres of infection, location of primary cases, sensitivity, proportion of asymptomatic and frequency or duration of lockdown, our simulator tracks every individual and hence infection progression through community over time. In a closed community of 10000 people, it is seen that without any lockdown, number of cases peak around 6th week and wanes off around 15th week. If primary case is located inside dense population cluster like slums, cases peak early and wane off slowly. With introduction of lockdown, cases peak at slower rate. If sensitivity of identifying infection decreases, cases and deaths increase. Number of cases declines with increase in proportion of asymptomatic cases. The model is robust and provides reproducible estimates with realistic parameter values. It also guides in identifying measures to control outbreak in a community. It is flexible in accommodating different parameters like infectivity period, yield of testing, socio-economic strata, daily travel, awareness level, population density, social distancing, lockdown etc. and can be tailored to study other infections with similar transmission pattern.
In this note, we discuss the impact of the COVID-19 outbreak from the perspective of the market-structure. We observe that the US market-structure has dramatically changed during the past four weeks and that the level of change has followed the number of infected cases reported in the USA. Presently, market-structure resembles most closely the structure during the middle of the 2008 crisis but there are signs that it may be starting to evolve into a new structure altogether. This is the first article of a series where we will be analyzing and discussing market-structure as it evolves to a state of further instability or, more optimistically, stabilization and recovery.
Model selection is a fundamental part of the applied Bayesian statistical methodology. Metrics such as the Akaike Information Criterion are commonly used in practice to select models but do not incorporate the uncertainty of the models parameters and can give misleading choices. One approach that uses the full posterior distribution is to compute the ratio of two models normalising constants, known as the Bayes factor. Often in realistic problems, this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a variation of the TI method, referred to as referenced TI, which computes a single models normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with explicit pedagogical 1 and 2D examples. Benchmarking is presented with comparable methods and we find favourable convergence performance. The approach is shown to be useful in practice when applied to a real problem - to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density.
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