The effect of governance structures on optimal control of two-patch epidemic models.

Infectious diseases continue to pose a significant threat to the health of humans globally. While the spread of pathogens transcends geographical boundaries, the management of infectious diseases typically occurs within distinct spatial units, determined by geopolitical boundaries. The allocation of management resources within and across regions (the "governance structure") can affect epidemiological outcomes considerably, and policy-makers are often confronted with a choice between applying control measures uniformly or differentially across regions. Here, we investigate the extent to which uniform and non-uniform governance structures affect the costs of an infectious disease outbreak in two-patch systems using an optimal control framework. A uniform policy implements control measures with the same time varying rate functions across both patches, while these measures are allowed to differ between the patches in a non-uniform policy. We compare results from two systems of differential equations representing transmission of cholera and Ebola, respectively, to understand the interplay between transmission mode, governance structure and the optimal control of outbreaks. In our case studies, the governance structure has a meaningful impact on the allocation of resources and burden of cases, although the difference in total costs is minimal. Understanding how governance structure affects both the optimal control functions and epidemiological outcomes is crucial for the effective management of infectious diseases going forward.

A Hybrid Epidemic Model to Explore Stochasticity in COVID-19 Dynamics.

The dynamic nature of the COVID-19 pandemic has demanded a public health response that is constantly evolving due to the novelty of the virus. Many jurisdictions in the USA, Canada, and across the world have adopted social distancing and recommended the use of face masks. Considering these measures, it is prudent to understand the contributions of subpopulations-such as "silent spreaders"-to disease transmission dynamics in order to inform public health strategies in a jurisdiction-dependent manner. Additionally, we and others have shown that demographic and environmental stochasticity in transmission rates can play an important role in shaping disease dynamics. Here, we create a model for the COVID-19 pandemic by including two classes of individuals: silent spreaders, who either never experience a symptomatic phase or remain undetected throughout their disease course; and symptomatic spreaders, who experience symptoms and are detected. We fit the model to real-time COVID-19 confirmed cases and deaths to derive the transmission rates, death rates, and other relevant parameters for multiple phases of outbreaks in British Columbia (BC), Canada. We determine the extent to which SilS contributed to BC's early wave of disease transmission as well as the impact of public health interventions on reducing transmission from both SilS and SymS. To do this, we validate our model against an existing COVID-19 parameterized framework and then fit our model to clinical data to estimate key parameter values for different stages of BC's disease dynamics. We then use these parameters to construct a hybrid stochastic model that leverages the strengths of both a time-nonhomogeneous discrete process and a stochastic differential equation model. By combining these previously established approaches, we explore the impact of demographic and environmental variability on disease dynamics by simulating various scenarios in which a COVID-19 outbreak is initiated. Our results demonstrate that variability in disease transmission rate impacts the probability and severity of COVID-19 outbreaks differently in high- versus low-transmission scenarios.

A vaccination model for COVID-19 in Gauteng, South Africa.

The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.

Data-driven models for replication kinetics of Orthohantavirus infections.

The Hantaviridae constitute a family of viruses harbored by mice, rats, shrews, voles, moles and bats. Intriguingly, only viruses harbored by mice and rats may cause disease in humans with up to 40% case fatality rate in the Americas. Transmission of virus from rodents to humans occurs via the respiratory route and results in replication of the virus in the microvascular endothelial cells of the lung or kidney. Understanding the replication kinetics of these viruses in various cell types and how replication is abrogated by the host is critical to the development of effective therapeutics for treatment for which there are none. We formulate several new ordinary differential equation (ODE) models to examine the replication kinetics of Prospect Hill orthohantavirus (PHV). The models are distinguished by the distribution of the viral replication delay. A new threshold, RGE, the genome equivalent replication number, is defined in terms of the model parameters. New final density relations are derived that associate RGE to the asymptotic number of virions in each model. All models are fit to real time (qRT)-PCR data of genomic RNA from PHV released from Vero E6 cells over 192 h. A sensitivity analysis of the parameters is performed and models are tested for best fit. Our findings provide a basis for future research into formulating more complex mathematical models for evaluation of the replication of hantaviruses in various cell types and sources.

Effects of environmental variability on superspreading transmission events in stochastic epidemic models.

Superspreaders (individuals with a high propensity for disease spread) have played a pivotal role in recent emerging and re-emerging diseases. In disease outbreak studies, host heterogeneity based on demographic (e.g. age, sex, vaccination status) and environmental (e.g. climate, urban/rural residence, clinics) factors are critical for the spread of infectious diseases, such as Ebola and Middle East Respiratory Syndrome (MERS). Transmission rates can vary as demographic and environmental factors are altered naturally or due to modified behaviors in response to the implementation of public health strategies. In this work, we develop stochastic models to explore the effects of demographic and environmental variability on human-to-human disease transmission rates among superspreaders in the case of Ebola and MERS. We show that the addition of environmental variability results in reduced probability of outbreak occurrence, however the severity of outbreaks that do occur increases. These observations have implications for public health strategies that aim to control environmental variables.

A county-level analysis of persons living with HIV in the southern United States.

This study uses county-level surveillance data to systematically analyze geographic variation and clustering of persons living with diagnosed HIV (PLWH) in the southern United States in 2011. Clusters corresponding to large metropolitan areas - including Miami, Atlanta, and Baltimore - had HIV prevalence rates higher (p < .001) than the regional rate. Regression analysis within the counties included in these clusters determined that race was a significant indicator for PLWH. These results provide a general picture of the distribution of PLWH in the southern United States at the county level and provide insights for identifying local geographic areas with a high number of PLWH, as well as subpopulations that may have an increased risk of infection.