COVID-19 dynamics in an Ohio prison

Abstract

ImportanceIncarcerated individuals are a vulnerable population for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Understanding SARS-CoV-2 dynamics in prisons is crucial for curbing transmission both within correctional facilities and in the surrounding community.ObjectiveThe purpose of this study was to identify transmission scenarios that could underly rapid, widespread SARS-CoV-2 infection among inmates in Marion Correctional Institution (MCI).DesignPublicly available data reported by the Ohio Department of Rehabilitation and Corrections (ODRC) was analyzed using mathematical and statistical models.SettingWe consider SARS-CoV-2 transmission dynamics among MCI inmates prior to and including April 16, 2020.ParticipantsThis study uses de-identified, publicly available SARS-CoV-2 test result data for MCI inmates.ExposuresInmates at MCI were considered exposed to potential infection with SARS-CoV-2.Main outcome and measuresResults from mass testing conducted on April 16, 2020 were analyzed together with time of first reported SARS-CoV-2 infection among MCI inmates.ResultsRapid, widespread infection of MCI inmates was reported, with nearly 80% of inmates infected within three weeks of first reported inmate case. These data are consistent with i) a basic reproduction number greater than 14, together with a single initially infected inmate, ii) an initial super-spreading event resulting in several hundred initially infected inmates, together with a basic reproduction number of approximately three, and iii) earlier undetected circulation of virus among inmates prior to April.Conclusions and relevanceMass testing data are consistent with extreme transmissibility, super-spreading events, or undetected circulation of virus among inmates. All scenarios consistent with these data attest to the vulnerabilities of prisoners to COVID-19.Key pointsQuestionTo identify transmission characteristics consistent with timing and extent of SARS-CoV-2 infection among inmates in Marion Correctional Institution.FindingsMathematical and statistical modeling finds three scenarios that are consistent with the observed widespread infection in Marion Correctional Institution: i) very high transmissibility corresponding to a basic reproduction number in the double digits, ii) an initial super-spreading event involving exposure of several hundred inmates, iii) undetected circulation of virus prior to the first documented case among inmates.MeaningHigh transmissibility, super-spreading events, and challenges with disease surveillance all attest to the vulnerabilities of prison populations to SARS-CoV-2.

Concerns about the COVID‐19 Pandemic Among Justice‐Involved and Low‐Income Youth

“…However, these are likely underestimates, given that many juvenile facilities have been reticent to release their COVID‐19 case numbers (Rovner, 2021). Given what is known about the rate of spread of COVID‐19 in other congregate carceral institutions (i.e., adult prisons, jails, and migrant detention facilities, which have been labeled as superspreader contexts; see KhudaBukhsh et al., 2021; Tosh et al., 2021), it is likely that the COVID‐19 case rate among detained youth is disproportionately higher than that among community adolescents.…”

Individual molecules dynamics in reaction network models

“…Thus, from the viewpoint of a single, randomly selected S-type molecule, the quantity s t defines a survival function describing the limiting probability of surviving beyond time t > 0. The formula (1.3) led to the method of approximating the distribution of surviving molecules of S dubbed 'dynamic survival analysis' (DSA) described in [9] and applied recently to epidemic modeling [8,13]. The idea is further illustrated in Figure 1.…”

“…Finally, the independence of the single-molecule trajectories also allows for much simplified statistical inferential procedures. Such applications were already considered in the context of SIR networks in recent papers on the Covid pandemic [8,13]. A thorough investigation of these techniques in general reaction networks is currently being conducted and will appear in a future work.…”

Towards a quantitative reduction of the SIR epidemiological model

“…This work belongs to the recent attempts of the applied mathematics community to understand, from a more fundamental perspective, the spread of viruses, like Covid-19, which are drastically affecting the well-being of our society; see e.g. [2,28,1,31,32,26], citing but a few. Our own motivation stems from the potential use of SIR-type epidemiological models in the context of dynamic networks as provided by large-scale highly interacting inhomogeneous human crowds.…”