ABSTRACT
Background: Despite increased social vulnerability and barriers to care, there has been a paucity of data on SARS-CoV-2 incidence among key populations in sub-Saharan Africa. We seek to characterize active infections and define transmission dynamics of SARS-CoV-2 among people who inject drugs (PWID) and their sexual and injecting partners from Nairobi and the coastal region in Kenya. Method(s): This was a nested cross-sectional study of SARS-CoV-2 infection from April to July 2021 within a cohort study of assisted partner services for PWID in Kenya. A total of 1000 PWID and their partners (500 living with and 500 living without HIV) were recruited for SARS-CoV-2 antibody testing, of whom 440 were randomly selected to provide self-collected nasal swabs for real-time PCR testing. Whole genome sequencing (WGS) was completed on a limited subset of samples (N=23) with cycle threshold values 32.0. Phylogenetic tree construction and analysis was performed using the Nextstrain pipeline and compared with publicly available SARS-CoV-2 sequences from GenBank. Result(s): A total of 438 (99.5%) participants provided samples for SARS-CoV-2 PCR testing. Median age was 37 (IQR 32-42);128 (29.2%) were female;and 222 (50.7%) were living with HIV. The overall prevalence of SARS-CoV-2 infection identified by RT-PCR was 86 (19.6%). In univariate analyses, there was no increased relative risk of SARSCoV- 2 infection related to positive HIV status, frequenting an injection den, methadone treatment, unstable housing, report of any high-risk exposure, or having a sexual or injecting partner diagnosed with COVID-19 or who died from COVID-19 or flu-like illness. Eight samples were successfully sequenced via WGS and classified as WHO variants of concern: 3 Delta, 3 Alpha, and 2 Beta. Seven were classified into clades predominantly circulating in Kenya during 2021. Notably, two sequences were identical and matched identically to another Kenyan sequence, which is consistent with, though not indictive of, a transmission linkage. Conclusion(s): Overall, the risk of SARS-CoV-2 infection in this population of PWID and their partners was not significantly associated with risk factors related to injection drug use. At a genomic level, the SARS-CoV-2 strains in this study were consistent with contemporary Kenyan lineages circulating during the time and not unique to PWID. Prevention efforts, therefore, must also focus on marginalized groups for control given the substantial amount of mixing that likely occurs between populations.
ABSTRACT
Background. Mask mandates have been a widely used public health tool during the COVID-19 pandemic, but how to optimize their impact in the setting of concurrent but spontaneous population-level behavior changes due to rising case counts is not known. This study aimed to examine how earlier or later mask mandate implementation in the context of spontaneous behavior change would have affected transmission of SARS-CoV-2 and severe COVID-19 outcomes in the St. Louis, Missouri area. Methods. Our model utilized aggregated hospitalization and death data for St. Louis city and county residents admitted to nearly all hospitals in the metropolitan area. We first fit a real-life model to estimate changes in transmission after the July 3, 2020 mask mandate, and then created counterfactual scenarios in which 1) 10%, 25%, and 50% of the changes were attributed to the mandate (as opposed to spontaneous behavior change) and 2) the mandate was implemented 3 or 7 days earlier, or 7 or 14 days later. We used an SEIR (Susceptible-Exposed-Infectious-Recovered) model framework and fit models in R. Results. Assuming that 50% of increased masking was due to the mandate, implementing a mandate 7 days earlier was associated with a reduction from 12,685 (IQR: 10,463-16,560) to 12,294 (10,296-15,205) cumulative hospitalizations by September 30, while a 2-week delay was associated with an increase to 13,277 (10,808-17,908) hospitalizations. Trends were similar, but with reduced magnitude, when assuming that 10% or 25% of increased masking was due to the mandate (Figure). Depending on whether 10%, 25%, or 50% of increased masking was due to the mandate, implementing the mandate 1 week early was associated with a return to baseline (June 26) hospital census 1-7 days earlier, while delaying the mandate by 2 weeks led to a 2-12 day delay in return to baseline. Hospital census and cumulative deaths in the real-life (baseline) model and under 12 counterfactual scenarios which vary mask mandate timing (3 or 7 days earlier, or 7 or 14 days delayed) and percentage of increase in masking that is attributed to the mask mandate (Panels A-B: 10%, Panels C-D: 25%, and Panels E-F: 50%). As more of the increase in masking is attributed to the mandate, the costs of delaying the mandate and the benefits of earlier implementation increase. While differences in hospital census are most apparent several weeks after the mandate, differences in deaths gradually become more apparent over time. Conclusion. Impact of a mask mandate depends on both timing and percent of increased masking that is attributed to the mandate. Implementing a mandate even a few days earlier is associated with fewer cumulative hospitalizations and earlier return to baseline, but the overall duration of implementation is slightly longer. Given wide variations in public behavior, locally-tailored models are essential for estimating the impact of interventions and informing the local public health response.