Modeling county level COVID-19 transmission in the greater St. Louis area: Challenges of uncertainty and identifiability when fitting mechanistic models to time-varying processes.

We use a compartmental model with a time-varying transmission parameter to describe county level COVID-19 transmission in the greater St. Louis area of Missouri and investigate the challenges in fitting such a model to time-varying processes. We fit this model to synthetic and real confirmed case and hospital discharge data from May to December 2020 and calculate uncertainties in the resulting parameter estimates.

Modeling impact of vaccination on COVID-19 dynamics in St. Louis.

The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St.

Early coronavirus disease 2019 (COVID-19) pandemic effects on individual-level risk for healthcare-associated infections in hospitalized patients.

Objective: We compared the individual-level risk of hospital-onset infections with multidrug-resistant organisms (MDROs) in hospitalized patients prior to and during the coronavirus disease 2019 (COVID-19) pandemic. We also quantified the effects of COVID-19 diagnoses and intrahospital COVID-19 burden on subsequent MDRO infection risk.

Design: Multicenter, retrospective, cohort study.

Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: An agent-based modeling approach.

Nonpharmaceutical interventions for minimizing indoor SARS-CoV-2 transmission continue to be critical tools for protecting susceptible individuals from infection, even as effective vaccines are produced and distributed globally. We developed a spatially-explicit agent-based model for simulating indoor respiratory pathogen transmission during discrete events taking place in a single room within a sub-day time frame, and used it to compare effects of four interventions on reducing secondary SARS-CoV-2 attack rates during a superspreading event by simulating a well-known case study. We found that preventing people from moving within the simulated room and efficacious mask usage appear to have the greatest effects on reducing infection risk, but multiple concurrent interventions are required to minimize the proportion of susceptible individuals infected.

Combining epidemiological and ecological methods to quantify social effects on transmission.

Enteric microparasites like use multiple transmission pathways to propagate within and between host populations. Characterizing the relative transmission risk attributable to host social relationships and direct physical contact between individuals is paramount for understanding how microparasites like spread within affected communities and estimating colonization rates. To measure these effects, we carried out commensal transmission experiments in two cattle () herds, wherein all individuals were equipped with real-time location tracking devices.

Modelling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies.

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and shipment restrictions. Here, we developed a nested multiscale model for the transmission of ASF, combining a spatially explicit network model of animal shipments with a deterministic compartmental model for the dynamics of two ASF strains within 3 km × 3 km pixels in one Brazilian state.

When can we stop wearing masks? Agent-based modeling to identify when vaccine coverage makes nonpharmaceutical interventions for reducing SARS-CoV-2 infections redundant in indoor gatherings.

As vaccination efforts to combat the COVID-19 pandemic are ramping up worldwide, there are rising concerns that individuals will begin to eschew nonpharmaceutical interventions for preventing SARS-CoV-2 transmission and attempt to return to pre-pandemic normalcy before vaccine coverage levels effectively mitigate transmission risk. In the U.S.

Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: an agent-based modeling approach.

Unlabelled: Intervention strategies for minimizing indoor SARS-CoV-2 transmission are often based on anecdotal evidence because there is little evidence-based research to support them. We developed a spatially-explicit agent-based model for simulating indoor respiratory pathogen transmission, and used it to compare effects of four interventions on reducing individual-level SARS-CoV-2 transmission risk by simulating a well-known case study. We found that imposing movement restrictions and efficacious mask usage appear to have the greatest effects on reducing infection risk, but multiple concurrent interventions are required to minimize the proportion of susceptible individuals infected.

Accounting for space and uncertainty in real-time location system-derived contact networks.

Point data obtained from real-time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity-based definitions of interanimal "contact," however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining "contact" as polygon intersections.

Transmission on empirical dynamic contact networks is influenced by data processing decisions.

Dynamic contact data can be used to inform disease transmission models, providing insight into the dynamics of infectious diseases. Such data often requires extensive processing for use in models or analysis. Therefore, processing decisions can potentially influence the topology of the contact network and the simulated disease transmission dynamics on the network.