kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates.

The kppm is a standard procedure to estimate the parameters of the inhomogeneous Cox point process. However, the procedure cannot handle the problem when the models involve correlated covariates. In this study, we develop the kppmenet, the modified version of the kppm, for the inhomogeneous Cox point process involving correlated covariates by considering elastic net regularization. We compare the methodology in a simulation study and apply it to model major-shallow earthquake distribution in Sumatra, Indonesia. We conclude that the kppmenet outperforms kppm when correlated covariates are involved.

COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach.

Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.