The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.
In this article, we examine the role of nongovernmental entities (NGEs; nonprofits, religious groups, and businesses) in disaster response and recovery. Although media reports and the existing scholarly literature focus heavily on the role of governments, NGEs provide critical services related to public safety and public health after disasters. NGEs are crucial because of their ability to quickly provide services, their flexibility, and their unique capacity to reach marginalized populations. To examine the role of NGEs, we surveyed 115 NGEs engaged in disaster response. We also conducted extensive field work, completing 44 hours of semistructured interviews with staff from NGEs and government agencies in postdisaster areas in Texas, Florida, Puerto Rico, Northern California, and Southern California. Finally, we compiled quantitative data on the distribution of nonprofit organizations. We found that, in addition to high levels of variation in NGE resources across counties, NGEs face serious coordination and service delivery problems. Federal funding for expanding the capacity of local Voluntary Organizations Active in Disaster groups, we suggest, would help NGEs and government to coordinate response efforts and ensure that recoveries better address underlying social and economic vulnerabilities.
Until the 1930s, malaria was endemic throughout large swaths of the American South. We used a Poisson mixture model to analyze the decline of malaria at the county level in Alabama (an archetypical Deep South cotton state) during the 1930s. Employing a novel data set, we argue that, contrary to a leading theory, the decline of malaria in the American South was not caused by population movement away from malarial areas or the decline of Southern tenant farming. We elaborate and provide evidence for an alternate explanation that emphasizes the role of targeted New Deal-era public health interventions and the development of local-level public health infrastructure. We show that, rather than disappearing as a consequence of social change or economic improvements, malaria was eliminated in the Southern United States in the face of economic dislocation and widespread and deep-seated poverty.
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