Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly. Highlights: We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

Hitting Times of Some Critical Events in RNA Origins of Life.

Can a replicase be found in the vast sequence space by random drift? We partially answer this question through a proof-of-concept study of the times of occurrence (hitting times) of some critical events in the origins of life for low-dimensional RNA sequences using a mathematical model and stochastic simulation studies from Python software. We parameterize fitness and similarity landscapes for polymerases and study a replicating population of sequences (randomly) participating in template-directed polymerization. Under the ansatz of localization where sequence proximity correlates with spatial proximity of sequences, we find that, for a replicating population of sequences, the hitting and establishment of a high-fidelity replicator depends critically on the polymerase fitness and sequence (spatial) similarity landscapes and on sequence dimension. Probability of hitting is dominated by landscape curvature, whereas hitting time is dominated by sequence dimension. Surface chemistries, compartmentalization, and decay increase hitting times. Compartmentalization by vesicles reveals a trade-off between vesicle formation rate and replicative mass, suggesting that compartmentalization is necessary to ensure sufficient concentration of precursors. Metabolism is thought to be necessary to replication by supplying precursors of nucleobase synthesis. We suggest that the dynamics of the search for a high-fidelity replicase evolved mostly during the final period and, upon hitting, would have been followed by genomic adaptation of genes and to compartmentalization and metabolism, effecting degree-of-freedom gains of replication channel control over domain and state to ensure the fidelity and safe operations of the primordial genetic communication system of life.