Intercept and Recall: Examining Avidity Carryover in On-Site Collected Travel Data

This study examines the proper estimation of trip demand and economic benefits for visitors to recreation sites when past-season trip information is elicited from travelers intercepted on-site. We show that the proper weighting of past season counts is different from the standard on-site correction appropriate for current-season counts. We find that for our sample of lake visitors relatively stronger preference or “avidity” for the interview site carries over across seasons. We further show that using the correct weighting of past trip counts is critical in deriving meaningful estimates of travel demand and economic benefits.On-site Sampling; Recreation Demand Systems; Poisson-Lognormal Distribution; Simulated Maximum Likelihood

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.Bayesian, model selection, model averaging, count data, zero-inflation, demand for health care

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.Bayesian, model selection, model averaging, count data, zero-inflation, demand for health care

Quantifying superspreading for COVID-19 using Poisson mixture distributions.

The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, [Formula: see text], while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution

Increasing Beach Recreation Benefits by Using Wetlands to Reduce Contamination

The public swimming beach at Maumee Bay State Park (MBSP) on Lake Erie is often posted for occurrences of unsafe levels of bacteria. The main source of bacteria derives from a drainage ditch that discharges near the beach. We have conducted a comprehensive study to determine the feasibility of using a constructed wetland to filter the ditch water, prior to its entry into Maumee Bay. As part of this study, we administered an on-site non-market valuation survey of beach visitors, in which observed and contingent trips to the beach were used to estimate the potential welfare benefits of the restored wetlands. The data were analyzed using three versions of the multivariate Poisson-lognormal (MPLN) model, a random effects count data model. We conclude version one, with flexible covariance structure and vehicle costs of $0.25 per mile, is the preferred version and use it to estimate an average annual willingness to pay (WTP) of$166 to construct wetlands and improve water quality. The aggregate annual benefit to an estimated 37,300 annual beach visitors is estimated as $6.19 million. The robustness of this estimate to a variety of alternative assumptions is examined.Count data model, Poisson lognormal, on-site sampling, recreation demand, wetland, simulated maximum likelihood, Community/Rural/Urban Development, Environmental Economics and Policy, Public Economics, Q51,

A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread

First-order multivariate integer-valued autoregressive model with multivariate mixture distributions

The univariate integer-valued time series has been extensively studied, but literature on multivariate integer-valued time series models is quite limited and the complex correlation structure among the multivariate integer-valued time series is barely discussed. In this study, we proposed a first-order multivariate integer-valued autoregressive model to characterize the correlation among multivariate integer-valued time series with higher flexibility. Under the general conditions, we established the stationarity and ergodicity of the proposed model. With the proposed method, we discussed the models with multivariate Poisson-lognormal distribution and multivariate geometric-logitnormal distribution and the corresponding properties. The estimation method based on EM algorithm was developed for the model parameters and extensive simulation studies were performed to evaluate the effectiveness of proposed estimation method. Finally, a real crime data was analyzed to demonstrate the advantage of the proposed model with comparison to the other models