Dynamics of bed bug infestations and control under disclosure policies

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2019-01-01
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Xie, Sherrie
Hill, Alison
Levy, Michael
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Research Projects
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Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost–benefit relationships.

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This article is published as Xie, Sherrie, Alison L. Hill, Chris R. Rehmann, and Michael Z. Levy. "Dynamics of bed bug infestations and control under disclosure policies." Proceedings of the National Academy of Sciences (2019): 201814647. DOI: 10.1073/pnas.1814647116 . Posted with permission.

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Tue Jan 01 00:00:00 UTC 2019
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